{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional\t\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM \n",
    "关于nn.LSTM的参数，官方文档给出的解释为：\n",
    "\n",
    "![](LSTM.png)\n",
    "\n",
    "总共有七个参数，其中只有前三个是必须的。由于大家普遍使用PyTorch的DataLoader来形成批量数据，因此batch_first也比较重要。LSTM的两个常见的应用场景为文本处理和时序预测，因此下面对每个参数我都会从这两个方面来进行具体解释。\n",
    "\n",
    "input_size：在文本处理中，由于一个单词没法参与运算，因此我们得通过Word2Vec来对单词进行嵌入表示，将每一个单词表示成一个向量，此时input_size=embedding_size。比如每个句子中有五个单词，每个单词用一个100维向量来表示，那么这里input_size=100；在时间序列预测中，比如需要预测负荷，每一个负荷都是一个单独的值，都可以直接参与运算，因此并不需要将每一个负荷表示成一个向量，此时input_size=1。 但如果我们使用多变量进行预测，比如我们利用前24小时每一时刻的[负荷、风速、温度、压强、湿度、天气]来预测下一时刻的负荷，那么此时input_size=7。\n",
    "\n",
    "hidden_size：隐藏层节点个数。可以随意设置。\n",
    "\n",
    "num_layers：层数。nn.LSTMCell与nn.LSTM相比，num_layers默认为1。\n",
    "\n",
    "batch_first：默认为False。\n",
    "\n",
    "\n",
    "\n",
    "## Inputs\n",
    "关于LSTM的输入，官方文档给出的定义为：\n",
    "\n",
    " ![](Input.png)\n",
    "\n",
    " \n",
    " 可以看到，输入由两部分组成：input、(初始的隐状态h_0，初始的单元状态c_0)\n",
    "\n",
    "​\n",
    "其中input：\n",
    "\n",
    "input(seq_len, batch_size, input_size)\n",
    "\n",
    "seq_len：在文本处理中，如果一个句子有7个单词，则seq_len=7；在时间序列预测中，假设我们用前24个小时的负荷来预测下一时刻负荷，则seq_len=24。\n",
    "\n",
    "batch_size：一次性输入LSTM中的样本个数。在文本处理中，可以一次性输入很多个句子；在时间序列预测中，也可以一次性输入很多条数据。\n",
    "\n",
    "input_size：见前文。\n",
    "\n",
    "(h_0, c_0)：\n",
    "\n",
    "h_0(num_directions * num_layers, batch_size, hidden_size)\n",
    "\n",
    "c_0(num_directions * num_layers, batch_size, hidden_size)\n",
    "\n",
    "h_0和c_0的shape一致。\n",
    "\n",
    "num_directions：如果是双向LSTM，则num_directions=2；否则num_directions=1。\n",
    "\n",
    "num_layers：见前文。\n",
    "\n",
    "batch_size：见前文。\n",
    "\n",
    "hidden_size：见前文。\n",
    "\n",
    "## Outputs\n",
    "\n",
    "关于LSTM的输出，官方文档给出的定义为：\n",
    "\n",
    "![](Output.png)\n",
    "\n",
    "可以看到，输出也由两部分组成：otput、(隐状态h_n，单元状态c_n)\n",
    "\n",
    "其中output的shape为：\n",
    "\n",
    "output(seq_len, batch_size, num_directions * hidden_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year     month  passengers\n",
      "0  1949   January         112\n",
      "1  1949  February         118\n",
      "2  1949     March         132\n",
      "3  1949     April         129\n",
      "4  1949       May         121\n",
      "(144, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "导入数据\n",
    "\"\"\"\n",
    "# flight_data = sns.load_dataset(\"flights\")\n",
    "flight_data=pd.read_csv('flights.csv')\n",
    "print(flight_data.head())\n",
    "print(flight_data.shape)\n",
    "\n",
    "#绘制每月乘客的出行频率\n",
    "fig_size = plt.rcParams['figure.figsize']\n",
    "fig_size[0] = 15\n",
    "fig_size[1] = 5\n",
    "plt.rcParams['figure.figsize'] = fig_size\n",
    "plt.title('Month vs Passenger')\n",
    "plt.ylabel('Total Passengers')\n",
    "plt.xlabel('Months')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x',tight=True)\n",
    "plt.plot(flight_data['passengers'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['year', 'month', 'passengers'], dtype='object')\n",
      "132\n",
      "12\n",
      "[[-0.96483516]\n",
      " [-0.93846154]\n",
      " [-0.87692308]\n",
      " [-0.89010989]\n",
      " [-0.92527473]]\n",
      "[[1.        ]\n",
      " [0.57802198]\n",
      " [0.33186813]\n",
      " [0.13406593]\n",
      " [0.32307692]]\n",
      "[(tensor([-0.9648, -0.9385, -0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066,\n",
      "        -0.8593, -0.9341, -1.0000, -0.9385]), tensor([-0.9516])), (tensor([-0.9385, -0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593,\n",
      "        -0.9341, -1.0000, -0.9385, -0.9516]), tensor([-0.9033])), (tensor([-0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341,\n",
      "        -1.0000, -0.9385, -0.9516, -0.9033]), tensor([-0.8374])), (tensor([-0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341, -1.0000,\n",
      "        -0.9385, -0.9516, -0.9033, -0.8374]), tensor([-0.8637])), (tensor([-0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341, -1.0000, -0.9385,\n",
      "        -0.9516, -0.9033, -0.8374, -0.8637]), tensor([-0.9077]))]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "数据预处理\n",
    "\"\"\"\n",
    "print(flight_data.columns)#显示数据集中 列的数据类型\n",
    "all_data = flight_data['passengers'].values.astype(float)#将passengers列的数据类型改为float\n",
    "#划分测试集和训练集\n",
    "test_data_size = 12\n",
    "train_data = all_data[:-test_data_size]#除了最后12个数据，其他全取\n",
    "test_data = all_data[-test_data_size:]#取最后12个数据\n",
    "print(len(train_data))\n",
    "print(len(test_data))\n",
    "\n",
    "#最大最小缩放器进行归一化，减小误差，注意，数据标准化只应用于训练数据而不应用于测试数据\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "train_data_normalized = scaler.fit_transform(train_data.reshape(-1, 1))\n",
    "#查看归一化之后的前5条数据和后5条数据\n",
    "print(train_data_normalized[:5])\n",
    "print(train_data_normalized[-5:])\n",
    "\n",
    "\n",
    "#将数据集转换为tensor，因为PyTorch模型是使用tensor进行训练的，并将训练数据转换为输入序列和相应的标签\n",
    "train_data_normalized = torch.FloatTensor(train_data_normalized).view(-1)\n",
    "#view相当于numpy中的resize,参数代表数组不同维的维度；\n",
    "#参数为-1表示，这个维的维度由机器自行推断，如果没有-1，那么view中的所有参数就要和tensor中的元素总个数一致\n",
    "\n",
    "\n",
    "#定义create_inout_sequences函数，接收原始输入数据，并返回一个元组列表。\n",
    "def create_inout_sequences(input_data, tw):\n",
    "    inout_seq = []\n",
    "    L = len(input_data)\n",
    "    for i in range(L-tw):\n",
    "        train_seq = input_data[i:i+tw]\n",
    "        train_label = input_data[i+tw:i+tw+1]#预测time_step之后的第一个数值\n",
    "        inout_seq.append((train_seq, train_label))#inout_seq内的数据不断更新，但是总量只有tw+1个\n",
    "    return inout_seq\n",
    "\n",
    "\n",
    "train_window = 12#设置训练输入的序列长度为12，类似于time_step = 12\n",
    "train_inout_seq = create_inout_sequences(train_data_normalized, train_window)\n",
    "print(train_inout_seq[:5])#产看数据集改造结果\n",
    "\n",
    "\n",
    "\n",
    "# 注意：\n",
    "# create_inout_sequences返回的元组列表由一个个序列组成，\n",
    "# 每一个序列有13个数据，分别是设置的12个数据（train_window）+ 第13个数据（label）\n",
    "# 第一个序列由前12个数据组成，第13个数据是第一个序列的标签。\n",
    "# 同样，第二个序列从第二个数据开始，到第13个数据结束，而第14个数据是第二个序列的标签，依此类推。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"\n",
    "创建LSTM模型\n",
    "参数说明：\n",
    "1、input_size:对应的及特征数量，此案例中为1，即passengers\n",
    "2、output_size:预测变量的个数，及数据标签的个数\n",
    "2、hidden_layer_size:隐藏层的特征数，也就是隐藏层的神经元个数\n",
    "\"\"\"\n",
    "class LSTM(nn.Module):#注意Module首字母需要大写\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "\n",
    "        # 创建LSTM层和linear层，LSTM层提取特征，linear层用作最后的预测\n",
    "        ##LSTM算法接受三个输入：先前的隐藏状态，先前的单元状态和当前输入。\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
    "\n",
    "        #初始化隐含状态及细胞状态C，hidden_cell变量包含先前的隐藏状态和单元状态\n",
    "        self.hidden_cell = (torch.zeros(1, 1, self.hidden_layer_size),\n",
    "                            torch.zeros(1, 1, self.hidden_layer_size))\n",
    "                            # 为什么的第二个参数也是1\n",
    "                            # 第二个参数代表的应该是batch_size吧\n",
    "                            # h_0(num_directions * num_layers, batch_size, hidden_size)\n",
    "                            # c_0(num_directions * num_layers, batch_size, hidden_size)\n",
    "                            \n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq),1,-1), self.hidden_cell)  # (12,1,-1) input(seq_len, batch_size, input_size)\n",
    "        #lstm的输出是当前时间步的隐藏状态ht和单元状态ct以及输出lstm_out\n",
    "        #按照lstm的格式修改input_seq的形状，作为linear层的输入\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]#返回predictions的最后一个元素\n",
    "\n",
    "# forward方法：LSTM层的输入与输出：out, (ht,Ct)=lstm(input,(h0,C0)),其中\n",
    "# 一、输入格式：lstm(input,(h0, C0))\n",
    "# 1、input为（seq_len,batch,input_size）格式的tensor,seq_len即为time_step\n",
    "# 2、h0为(num_layers * num_directions, batch, hidden_size)格式的tensor，隐藏状态的初始状态\n",
    "# 3、C0为(seq_len, batch, input_size）格式的tensor，细胞初始状态\n",
    "# 二、输出格式：output,(ht,Ct)\n",
    "# 1、output为(seq_len, batch, num_directions*hidden_size）格式的tensor，包含输出特征h_t(源于LSTM每个t的最后一层)\n",
    "# 2、ht为(num_layers * num_directions, batch, hidden_size)格式的tensor，\n",
    "# 3、Ct为(num_layers * num_directions, batch, hidden_size)格式的tensor，\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM(\n",
      "  (lstm): LSTM(1, 100)\n",
      "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
      ")\n",
      "epoch:  1 loss:0.02330837\n",
      "epoch: 26 loss:0.00785383\n",
      "epoch: 51 loss:0.00380358\n",
      "epoch: 76 loss:0.00851136\n",
      "epoch:101 loss:0.00007703\n",
      "epoch:126 loss:0.00010903\n",
      "epoch:149 loss:0.0002734915\n"
     ]
    }
   ],
   "source": [
    "#创建LSTM()类的对象，定义损失函数和优化器\n",
    "model = LSTM()\n",
    "loss_function = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)#建立优化器实例\n",
    "print(model)\n",
    "\n",
    "\"\"\"\n",
    "模型训练\n",
    "batch-size是指1次迭代所使用的样本量；\n",
    "epoch是指把所有训练数据完整的过一遍；\n",
    "由于默认情况下权重是在PyTorch神经网络中随机初始化的，因此可能会获得不同的值。\n",
    "\"\"\"\n",
    "epochs = 150\n",
    "for i in range(epochs):\n",
    "    for seq, labels in train_inout_seq:\n",
    "        #清除网络先前的梯度值\n",
    "        optimizer.zero_grad()#训练模型时需要使模型处于训练模式，即调用model.train()。缺省情况下梯度是累加的，需要手工把梯度初始化或者清零，调用optimizer.zero_grad()\n",
    "        #初始化隐藏层数据\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                             torch.zeros(1, 1, model.hidden_layer_size))\n",
    "        #实例化模型\n",
    "        y_pred = model(seq)\n",
    "        #计算损失，反向传播梯度以及更新模型参数\n",
    "        single_loss = loss_function(y_pred, labels)#训练过程中，正向传播生成网络的输出，计算输出和实际值之间的损失值\n",
    "        single_loss.backward()#调用loss.backward()自动生成梯度，\n",
    "        optimizer.step()#使用optimizer.step()执行优化器，把梯度传播回每个网络\n",
    "\n",
    "    # 查看模型训练的结果\n",
    "    if i%25 == 1:\n",
    "        print(f'epoch:{i:3} loss:{single_loss.item():10.8f}')\n",
    "print(f'epoch:{i:3} loss:{single_loss.item():10.10f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.12527473270893097, 0.04615384712815285, 0.3274725377559662, 0.2835164964199066, 0.3890109956264496, 0.6175824403762817, 0.9516483545303345, 1.0, 0.5780220031738281, 0.33186814188957214, 0.13406594097614288, 0.32307693362236023]\n",
      "[0.38492950797080994, 0.4689473807811737, 0.9402549862861633, 1.2908443212509155, 1.5728191137313843, 1.8422683477401733, 2.0469720363616943, 2.1842753887176514, 2.2736387252807617, 2.3334248065948486, 2.3763887882232666, 2.4092090129852295]\n",
      "[[419.07146306]\n",
      " [438.18552913]\n",
      " [545.40800938]\n",
      " [625.16708308]\n",
      " [689.31634837]\n",
      " [750.61604911]\n",
      " [797.18613827]\n",
      " [828.42265093]\n",
      " [848.75281   ]\n",
      " [862.3541435 ]\n",
      " [872.12844932]\n",
      " [879.59505045]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAFNCAYAAAC5cXZ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAB95UlEQVR4nO3dd3jV5f3/8eedTcgik7Bnwt57CThx171HVRy1Vm1/Vtt+u2vt0tZqXXXgFvcCFRFE9t4rIYwkBEICSUhC5rl/f5wTGyE7ZyTH1+O6cuWcz3yfc3NC3rnv+30bay0iIiIiIiLiXwJ8HYCIiIiIiIi4n5I9ERERERERP6RkT0RERERExA8p2RMREREREfFDSvZERERERET8kJI9ERERERERP6RkT0RE/IIxxhpj+vk6DhERkbZCyZ6IiLiVMWafMabCGBN/0vYNroSslxvusdgYc2trr9PKGHq5Xk+x62ufMeZBX8YkIiJSm5I9ERHxhL3A1TVPjDFDgXDfheNRMdbaCJyv99fGmHN8HZCnGGOCfB2DiIg0nZI9ERHxhFeAG2o9vxF4ufYBxphoY8zLxpgjxpj9xphfGWMCXPtuMsYsNcb83RhzzBiz1xgzy7XvT8BU4AlXj9oTtS57hjEmzRhTYIx50hhjTg7MGNPFGHPCGBNba9tIY0yeMSbYGNPPGPO1MabQte2tprxga+0KYBswxBgzzhizwhVHjjHmCWNMiOtexhjzmDEm1xhTZIzZYowZ4tp3rjFmuzHmuDEm2xjzs1oxnm+M2ei65nJjzLBa+/YZY35mjNnsivstY0xYrf0PuOI4aIy5tfaQV2NMqOt9PmCMOWyMedoY08G1b7oxJssY83NjzCHgxaa8FyIi0jYo2RMREU9YCUQZYwYaYwKBq4BXTzrm30A00Ac4DWdyeHOt/eOBXUA88FfgeWOMsdb+EvgGuNtaG2GtvbvWOecDY4FhwBXA2ScHZq09CKwALq21+RrgHWttJfAH4AugE9DNFWeDXAncZGAwsAGoBu5zxT4ROB24y3X4WcA0IMX1+q8A8l37ngdut9ZGAkOAr1zXHwm8ANwOxAHPAB8ZY0JrhXEFcA7Q2/X6b3Kdew5wP3AG0A+YflL4j7hiGeHa3xX4da39nYFYoCcwu7H3QkRE2g4leyIi4ik1vXtnAjuA7JodtRLAh6y1x621+4B/ANfXOn+/tfY5a201MAdIBpIauecj1toCa+0BYBHOBKYur+MaZurq/bvKtQ2gEmdi08VaW2atXdrIPfOAo8B/gQettQutteustSuttVWu1/YMzoS25vqRwADAWGt3WGtzau0bZIyJstYes9aud22fDTxjrV1lra221s4ByoEJteJ43Fp70Fp7FPi41mu/AnjRWrvNWlsK/LbmBNdrnw3cZ609aq09Djzsej9qOIDfWGvLrbUnGnkvRESkDVGyJyIinvIKzh6zmzhpCCfOHq9gYH+tbftx9irVOFTzwJWkAEQ0cs9DtR6XNnD8u8BEY0wyzl42B87eQoAHAAOsNsZsM8b8sJF7xltrO1lrB1prHwcwxqQYYz4xxhwyxhThTKDiXa/lK+AJ4Ekg1xjzrDEmynWtS4Fzgf2uoaQTXdt7Aj91DeEsMMYUAN2BLk147V2AzFr7aj9OwDmXcl2t637m2l7jiLW2rJH3QERE2iAleyIi4hHW2v04C7WcC7x30u48/teDVqMHtXr/Grt8K2M7hnOo5pU4E9I3rbXWte+QtfY2a20XnMMm/2Oav6TDU8BOoL+1Ngr4Bc4Esub+j1trRwODcA6h/H+u7WustRcBicAHwFzXKZnAn6y1MbW+wq21bzQhlhycw1FrdK/1OA84AQyudd1oV8GZb8Nt+ssWEZG2RMmeiIh40i3ATGttSe2NrqGZc4E/GWMijTE9cc4rO3leX30O45zr1xqv4xxmehn/G8KJMeZyY0xNcnQMZ7LjaOa1I4EioNgYMwC4s9b1xxpjxhtjgoESoAxwGGNCjDHXGmOiXXMHi2rd9zngDtd5xhjT0RhznjEmsgmxzAVuds2fDAf+r2aHtdbhuvZjxphEV3xdjTGnzHUUEZH2R8meiIh4jLV2j7V2bT27f4wz2ckAluJMuF5o4qX/BVxmnJU6H29heB8B/YFD1tpNtbaPBVYZY4pdx/zEWpvRzGv/DGeP4XGcyVTtip5Rrm3HcA5dzQf+5tp3PbDPNfTzDuBaANd7eBvO4Z/HgHRcBVgaY62dDzyOcw5jOs7iOeCc8wfw85rtrvt+CaQ258WKiEjbZFyjVkREROR7wBgzENgKhFprq3wdj4iIeI569kRERPycMeYHrvX0OgF/AT5Woici4v+U7ImIiPi/24FcYA/ONQDvbPhwERHxBxrGKSIiIiIi4ofUsyciIiIiIuKHlOyJiIiIiIj4oSBfB9AaMTExtl+/5q5zK21ZSUkJHTt29HUY4kZqU/+jNvU/alP/ozb1P2pT/+SOdl23bl2etTahrn3tOtlLSkpi7dr6lm+S9mjx4sVMnz7d12GIG6lN/Y/a1P+oTf2P2tT/qE39kzva1Rizv759Hh3GaYz5iTFmqzFmmzHmXte2WGPMAmNMmut7J9d2Y4x53BiTbozZbIwZ5cnYRERERERE/JnHkj1jzBDgNmAcMBw43xjTD3gQWGit7Q8sdD0HmAX0d33NBp7yVGwiIiIiIiL+zpM9ewOBVdbaUtfCrV8DlwAXAXNcx8wBLnY9vgh42TqtBGKMMckejE9ERERERMRveTLZ2wpMNcbEGWPCgXOB7kCStTbHdcwhIMn1uCuQWev8LNc2ERERERERaSaPLqpujLkFuAsoAbYB5cBN1tqYWsccs9Z2MsZ8AjxirV3q2r4Q+Lm1du1J15yNc5gnCQkJo+fOneux+MX7iouLiYiI8HUY4kZqU/+jNvU/alP/ozb1P2pT/+SOdp0xY8Y6a+2YuvZ5tBqntfZ54HkAY8zDOHvrDhtjkq21Oa5hmrmuw7Nx9vzV6ObadvI1nwWeBUhNTbWqSuRfVGnK/6hN/Y/a1P+oTf2P2tT/qE39k6fb1dPVOBNd33vgnK/3OvARcKPrkBuBD12PPwJucFXlnAAU1hruKSIiIiIiIs3g6XX23jXGxAGVwI+stQXGmEeAua4hnvuBK1zHzsM5ry8dKAVu9nBsIiIiIiIifsvTwzin1rEtHzi9ju0W+JEn4xEREREREfm+8HTPnoiIiIiIiLibtZC1psFDlOyJiIiIiIi0F2VFsGUurH0RDm9t8FCPFmgRERERERERN8jZBB//BP4xAD79KZgAOP+fDZ6inj0REREREZG2KnsdfPk72Ps1BHWAoZfCmB9Cl1FgDPDDek9VsiciIiIiItLWHNkNX/0BdnwE4XFw1h9h5PXQIabJl1CyJyIiIiIi0lYUZsHiR2DjaxAcDtMfgok/gtDIZl9KyZ6IiIiIiIivWQsrnoSFvwcsjLsdpv4UIhJafEkleyIiIiIiIr5Ukg8f3Alpn0PqeXDOn6FTz1ZfVsmeiIiIiIiIr+xfDu/cAqV5MOtvMO42V+GV1lOyJyIiIiIi4m2OanrumwtfvwGdesGtX0LycLfeQsmeiIiIiIiIN50ogLk30Hvf1zD0cjj/sRYVYGmMFlUXERERERHxlsoT8MbVsH85O1Pvhkue80iiB+rZExERERER8Q5HNbx7KxxYAZc9z6G8OAa4aX5eXdSzJyIiIiIi4mnWwqc/hZ2fwDmPwJBLPX5LJXsiIiIiIiKe9vVfYN2LMOV+mHCHV26pZE9ERERERMST1jwPi/8MI66D03/ttdsq2RMREREREfGUHR/DvJ9Byjlwwb/ctoZeUyjZExERERER8YT8Pc4F07uOgctehEDv1sdUsiciIiIiIuIJnz0IgSFw5asQEu712yvZExERERERcbfdn0PaFzD9QYhM8kkISvZERERERETcqarc2asXnwLjZvssDC2qLiIiIiIi4k4rnoCjGXD9+xAU4rMw1LMnIiIiIiLiLoXZsOTvMOB86DvTp6F4NNkzxtxnjNlmjNlqjHnDGBNmjOltjFlljEk3xrxljAlxHRvqep7u2t/Lk7GJiIiIiIi43YJfg3XA2X/ydSSeS/aMMV2Be4Ax1tohQCBwFfAX4DFrbT/gGHCL65RbgGOu7Y+5jhMREREREWkf9i+Hre/A5J9Ap16+jsbjwziDgA7GmCAgHMgBZgLvuPbPAS52Pb7I9RzX/tON8eKKgyIiIiIiIi3lqIZ5D0B0d5h8r6+jATyY7Flrs4G/AwdwJnmFwDqgwFpb5TosC+jqetwVyHSdW+U6Ps5T8YmIiIiIiLjNuhfh8BY4648+WVOvLsZa65kLG9MJeBe4EigA3sbZY/db11BNjDHdgfnW2iHGmK3AOdbaLNe+PcB4a23eSdedDcwGSEhIGD137lyPxC++UVxcTEREhK/DEDdSm/oftan/UZv6H7Wp/1Gbtm0B1RVMWHkLJR17smn4H6CJAxTd0a4zZsxYZ60dU9c+Ty69cAaw11p7BMAY8x4wGYgxxgS5eu+6Admu47OB7kCWa9hnNJB/8kWttc8CzwKkpqba6dOne/AliLctXrwYtal/UZv6H7Wp/1Gb+h+1qf9Rm7Zxm9+GyiJCLvwj0/tMb/Jpnm5XT87ZOwBMMMaEu+benQ5sBxYBl7mOuRH40PX4I9dzXPu/sp7qdhQREREREXGX9XOcBVl6TfN1JN/hyTl7q3AO21wPbHHd61ng58D9xph0nHPynned8jwQ59p+P/Cgp2ITERERERFxi/w9sO8bGHUDBLStZcw9OYwTa+1vgN+ctDkDGFfHsWXA5Z6MR0RERERExK3WzwETCCOu9XUkp2hbqaeIiIiIiEh7UVUBG1+H1FkQ2dnX0ZxCyZ6IiIiIiEhL7J4PJUdg1I2NH+sDSvZERERERERaYt0ciOoG/U73dSR1UrInIiIiIiLSXMf2w56vYOR1EBDo62jqpGRPRERERESkuTa86vw+8jrfxtEAJXsiIiIiIiLNUV3lTPb6nQEx3X0dTb2U7ImIiIiIiDRH+pdw/CCMbpuFWWoo2RMREREREWmO9XOgYyKknOPrSBqkZE9ERERERKSpinJg9+cw8loIDPZ1NA1SsiciIiIiItJUG18FWw2jbvB1JI1SsiciIiIiItJUm96CXlMhto+vI2mUkj0REREREZGmyEuH/DQYeKGvI2kSJXsiIiIiIiJNsfsz5/fUtl2YpYaSPRERERERkabY/RkkDoaYHr6OpEmU7ImIiIiIiDTmxDHYvxxSzvZ1JE2mZE9ERERERKQx6QudVThTZ/k6kiZTsiciIiIiItKY3Z9BeDx0He3rSJpMyZ6IiIiIiEhDqqsgbQH0PwsCAn0dTZMp2RMREREREWlI5kooK2g3VThrKNkTERERERFpyO7PICAY+s70dSTNomRPRERERESkIbs+g15TIDTS15E0i5I9ERERERGR+uTvgfy0dlWFs4aSPRERERERkfrsmu/8ntK+5uuBB5M9Y0yqMWZjra8iY8y9xphYY8wCY0ya63sn1/HGGPO4MSbdGLPZGDPKU7GJiIiIiIg0ye7PIHEQdOrp60iazWPJnrV2l7V2hLV2BDAaKAXeBx4EFlpr+wMLXc8BZgH9XV+zgac8FZuIiIiIiEijThTAgRWQcravI2kRbw3jPB3YY63dD1wEzHFtnwNc7Hp8EfCydVoJxBhjkr0Un4iIiIiIyHelfwmOKkhpf/P1wHvJ3lXAG67HSdbaHNfjQ0CS63FXILPWOVmubSIiIiIiIt63+zMIj4NuY3wdSYsYa61nb2BMCHAQGGytPWyMKbDWxtTaf8xa28kY8wnwiLV2qWv7QuDn1tq1J11vNs5hniQkJIyeO3euR+MX7youLiYiIsLXYYgbqU39j9rU/6hN/Y/a1P+oTb3POKqZtPwG8uPGsnPgvR65hzvadcaMGeustXVmo0GtunLTzALWW2sPu54fNsYkW2tzXMM0c13bs4Hutc7r5tr2HdbaZ4FnAVJTU+306dM9Frh43+LFi1Gb+he1qf9Rm/oftan/UZv6H7WpD+xbBkuK6TztJjoPnu6RW3i6Xb0xjPNq/jeEE+Aj4EbX4xuBD2ttv8FVlXMCUFhruKeIiIiIiIj37J4PAcHQd6avI2kxj/bsGWM6AmcCt9fa/Agw1xhzC7AfuMK1fR5wLpCOs3LnzZ6MTUREREREpF5pC6DnJAiL8nUkLebRZM9aWwLEnbQtH2d1zpOPtcCPPBmPiIiIiIhIowoOwJGdMPJ6X0fSKt6qxikiIiIiItI+pC1wfu9/pm/jaCUleyIiIiIiIrWlfwnRPSA+xdeRtIqSPRERERERkRpV5ZDxtbNXzxhfR9MqSvZERERERERq7F8OlSXtfggnKNkTERERERH5n/QvITAEek/zdSStpmRPRERERESkRtoC6DkZQjr6OpJWU7InIiIiIiICcGw/5O3yiyGcoGRPRERERETEKd215EI/JXsiIiIiIiL+I+1LiOkJ8f19HYlbKNkTERERERGpKoe9/rHkQg0leyIiIiIiIvuXQWWp3wzhBCV7IiIiIiIiziGcgaHQe6qvI3EbJXsiIiIiIiLpC6CXfyy5UEPJnoiIiIiIfL8d2wd5u/1qCCco2RMRERERke+7NNeSC/3P8m0cbqZkT0REREREvt/Sv4ROvSCur68jcSsleyIiIiIi8v1VWQYZXzuHcPrJkgs1lOyJiIiIiMj31/5lUHXC74ZwgpI9ERERERH5PstYBIEh0GtK6y5zpJjC0ko3BeUeSvZEREREROT768Aq6DISQsJbfInKagcXPbmMcx//ht2Hj7sxuNZpVrJnjOlkjBnmqWBERERERES8prIMcjZC9/Gtuszuw8c5XlbF4aIyLn1qOcv35LknvlZqNNkzxiw2xkQZY2KB9cBzxphHPR+aiIiIiIiIBx3cANUV0GNCqy6zKbMQgJdvGUdydBg3vrCa99ZnuSPCVmlKz160tbYIuAR42Vo7HjjDs2GJiIiIiIh4WOZK5/dW9uxtyiwgJjyYiX3iePuOSYztFcv9czfx+MI0rLVuCLRlmpLsBRljkoErgE+ac3FjTIwx5h1jzE5jzA5jzERjTKwxZoExJs31vZPrWGOMedwYk26M2WyMGdWC1yMiIiIiItI0B1ZBXD/oGN+qy2zKKmB4txiMMUR3COalm8dxyaiuPLpgNw+8s5nKaoebAm6epiR7vwM+B9KttWuMMX2AtCZe/1/AZ9baAcBwYAfwILDQWtsfWOh6DjAL6O/6mg081eRXISIiIiIi0hzWQuYq6N66IZwl5VXsPnyc4d1jvt0WEhTAPy4fzk9O78/b67J4Z51vhnQ2mOwZYwKB7tbaYdbauwCstRnW2ksbu7AxJhqYBjzvOq/CWlsAXATMcR02B7jY9fginMNErbV2JRDj6lEUERERERFxr7w0OHEUerRuCOeW7EIcFkZ0j/7OdmMM957Rn9iOIWzKLGjVPVoqqKGd1tpqY8zVwGMtuHZv4AjwojFmOLAO+AmQZK3NcR1zCEhyPe4KZNY6P8u1LafWNowxs3H2/JGQkMDixYtbEJq0VcXFxWpTP6M29T9qU/+jNvU/alP/ozZ1v845CxgArM4JoLQV7+28vRUAFB/YzuJDO07ZnxxWxapd2SxefPSUfZ5u1waTPZdlxpgngLeAkpqN1tr1Tbj2KODH1tpVxph/8b8hmzXXsMaYZs1YtNY+CzwLkJqaaqdPn96c06WNW7x4MWpT/6I29T9qU/+jNvU/alP/ozb1gA/ehg6xjDv3WjCmxZeZm72O7rGFXHjWjDr3Ly/dwUvL9zFl6jSCAr87sNLT7dqUZG+E6/vva22zwMxGzssCsqy1q1zP38GZ7B02xiRba3NcwzRzXfuzge61zu/m2iYiIiIiIuJemSudVThbkeiBc9mFkT1i6t0/KDmKiioHGXklpCRFtupezdVogRZr7Yw6vhpL9LDWHgIyjTGprk2nA9uBj4AbXdtuBD50Pf4IuMFVlXMCUFhruKeIiIiIiIh7lORBfnqr5+vlHi8ju+AEI2oVZznZoC5RAGw/WNSqe7VEUxZVTzLGPG+Mme96PsgYc0sTr/9j4DVjzGacPYQPA48AZxpj0nCu1/eI69h5QAaQDjwH3NWcFyIiIiIiItIkma7Bh62sxLnZtZj68AaSvT7xHQkJCmB7jveTvaYM43wJeBH4pev5bpzz955v7ERr7UZgTB27Tq/jWAv8qAnxiIiIiIiItNyBlRAYAl1Gtuoym7IKCAwwDHb13tUlKDCAAZ0j22bPHhBvrZ0LOACstVVAtUejEhERERER8ZTMVZA8AoLDWnWZjZkFpCRFEh7ScB/aoOQotucU4ezf8p6mJHslxpg4nEVZqJlP59GoREREREREPKGyDA5uaPV8PWstmzILTllfry6DukRxtKSCw0XlrbpnczVlGOf9OIun9DXGLAMSgMs8GpWIiIiIiIgn5GyE6opWz9fbl19KUVkVw7vFNHrsoGRXkZacQjpHt643sTkaTfasteuNMacBqYABdllrKz0emYiIiIiIiLsdWOn83r11PXubMguAhouz1BiQ/L+KnDMHJLXqvs3RaLJnjLnkpE0pxphCYIu1Nreuc0RERERERNqkzFUQ2xciElp1mY2ZBXQIDqR/YkSjx0aEBtErLtzrFTmbMozzFmAisMj1fDqwDuhtjPm9tfYVD8UmIiIiIiLiPtY6k72Uc1p9qU1ZBQztGk1QYFPKoDjn7Xm7ImdTIgsCBlprL7XWXgoMwlmsZTzwc08GJyIiIiIibV9VtYO9eSW+DqNx+elQmt/qIZwVVQ62HSxieBOKs9QYlBzFvvxSisurWnXv5mhKstfdWnu41vNc17ajgObuiYiIiIh8j63ee5Tz/72Umf9YTNrh474Op2E18/V6tK44y85DRVRUORjRvVOTzxnkWotvpxeHcjZlGOdiY8wnwNuu55e6tnUECjwVmIiIiIiItF25RWX8ef5O3t+QTXxECNbChswC+idF+jq0+mWuhLAYiOvfqsv8rzhLc3r2nMduzyliTK/YVt2/qZqS7P0IZ4I32fX8ZeBd61wRcIanAhMRERERkbanqtrBnBX7eWzBbiqqHNw9ox93Tu/LuD996fU5ac12YJVzCGdA0+bZ1WdjZiHxESF0jenQ5HOSokKJ7Rji1feoKUsvWOAd15eIiIiIiHxPFZdXccXTK9ieU8T01AR+c8Fgesd3BGBgchTbDhb6OMIGlORDfhqMuLrVl9qUVcDwbjEYY5p8jjGGQclRXq3I2WhKa4y5xBiTZowpNMYUGWOOG2PaeMouIiIiIiLu9s3uI2zPKeKvlw7jxZvGfpvoAQx2VZt0OKwPI2xA1mrn91Yupl5UVsmeI8VNWl/vZIO6RLHz0HGqqh2tiqGpmtJ/+VfgQmtttLU2ylobaa2N8nRgIiIiIiLStqzae5QOwYFcPLLrKb1ag7tEU1JRzf6jpT6KrhGHtjq/Jw9v1WW2ZhVibdMWUz/ZoOQoKqocZHipcmlTkr3D1todHo9ERERERETatDX7jjKqZwwhQaemETXVJtvsUM7c7RDTE0IbXwS9IRuzCgAY3q3pxVlq1LxH3pq315Rkb60x5i1jzNWuIZ2XGGMu8XhkIiIiIiLSZhSVVbI9p4ix9VSSTEmKJDjQsK2tFmnJ3QGJg1p9mU2ZBfSKCycmPKTZ5/aJ70hIUIDX5u01pRpnFFAKnFVrmwXe80hEIiIiIiLS5qzbdwxrYVzvupO9kKAA+idGts1kr6rCWZwl9ZxWXabaYVm19ygzByS26PygwAAGdI70Ws9eU6px3uyNQEREREREpO1atfcowYGGkQ0sJD64SxSLduVirW1WpUqPO7oHHFWt7tnbmHmMgtJKZqS2LNkD57y9L7YfxrnogWc1pRpnijFmoTFmq+v5MGPMrzwemYiIiIiItBmr9+YzrFsMHUIC6z1mcJco8ooryD1e7sXImiB3u/N74sBWXearnbkEBhim9U9o8TUGdYniaEkFh4s8/x41Zc7ec8BDQCWAtXYzcJUngxIRERERkbbjREU1m7MK6x3CWWNwV2fRkjZXpCV3B5hAiOvfqst8tfMIo3t0Ijo8uMXXGJTsKtKS4/n3qCnJXri1dvVJ26o8EYyIiIiIiLQ9Gw4co8phG032BiZHYQxsy25j8/Zyd0BcXwgOa/ElDhWWsSOniBktnK9XY0Cy9ypyNiXZyzPG9MVZlAVjzGVAjkejEhERERGRNmPV3qMEGBjds/75egARoUH0iuvY9oq05G5v9RDORbtyAVpcnKWG8z0K90pFzqYkez8CngEGGGOygXuBOz0ZlIiIiIiItB2r9x5lUJcoosIaH744qEsU27wwRLHJKkrh6N5WF2dZtDOXLtFhpCS1bp0+cL5HbaJnz1qbYa09A0gABlhrp1hr9zXl4saYfcaYLcaYjcaYta5tscaYBcaYNNf3Tq7txhjzuDEm3Riz2RgzqhWvS0RERERE3KCiysGGzGP1rq93ssFdosg8eoLCE5UejqyJ8nYDtlU9e+VV1SxNz2PGgES3VBkdlBzFvvxSTlR5tiJnU6px/sQYU7PW3mPGmPXGmLMaO6+WGdbaEdbaMa7nDwILrbX9gYWu5wCzgP6ur9nAU824h4iIiIiIV3ijZH5bsiW7kLJKB+Mbma9XY3AXZ5EWb60l16jcHc7vCS1P9lbvPUppRXWrh3DWGNTFOW9vX6HDLderT1OGcf7QWluEc1H1OOB64JFW3PMiYI7r8Rzg4lrbX7ZOK4EYY0xyK+4jIiIiIuI2+cXl3Pziai54Yun3KuFbvfcoQLN69qANVeTM3Q6BIRDbp8WXWLTzCCFBAUzsG+eWkMb3jqNjSCDLD3q27mVTkr2afspzcSZj22pta4wFvjDGrDPGzHZtS7LW1hR4OQQkuR53BTJrnZvl2iYiIiIi4lPr9h/lvMeXsmjXEbZmF3Gkra0j50Gr9+bTLzGCuIjQJh0fHxFKUlRo2+rZi0+FwKAWX2LRrlwm9okjPKTl16itY2gQFwzvwqpDVRwv89xw16ZEu84Y8wXQG3jIGBMJNLW/cYq1NtsYkwgsMMbsrL3TWmuNMc36s4graZwNkJCQwOLFi5tzurRxxcXFalM/ozb1P2pT/6M29T9qU/ex1vL5vire3l1BbJjh6gEhvLGzgrc+X8rQBPf84t8UvmpTh7Ws3FPK+OSgZt2/c2gVq9MOsnhxgcdia6oJmRsojB7Mjha+f4dKHOzNO8HkhEq3tkH/wGoqquEfby9meveWr9vXkKb8C70FGAFkWGtLjTGxwM1Nubi1Ntv1PdcY8z4wDjhsjEm21ua4hmnmug7PBrrXOr2ba9vJ13wWeBYgNTXVTp8+vSmhSDuxePFi1Kb+RW3qf9Sm/kdt6n/Upu5ReKKSB97ZxOe7DnP24CT+dvlwHA7LG79fQHBib6af1tdrsfiqTbdmF3Li86X8YPIQpo9o+qC7dRW7+M/iPUyYPJWw4EAPRtiIskJYnEfY0OkkTZ3eoku8sHQvsJ3Z50+mR1y420I7zVpe3DqfDYUd+O31U9x23dqaMoxzIrDLWltgjLkO+BXQ6ABcY0xHVy8gxpiOOOf8bQU+Am50HXYj8KHr8UfADa6qnBOAwlrDPUVEREREvCbzaCkXPrGUhTty+dV5A3n6utFEhQUTEx5CcnQYO72wRlpb0Nz5ejUGd4mi2mHZdei4J8JqulzXwMJWLLuwaFcufRM6ujXRAzDGcFq3YDZlFXpsyGtTkr2ngFJjzHDgp8Ae4OUmnJcELDXGbAJWA59aaz/DWdzlTGNMGnAG/yv2Mg/IANKB54C7mvNCRERERETc5fmlezlUWMZbt0/g1ql9vlNuf2ByFDtyfJzEeMnqvUfp1qkDXWI6NOu8moqcPl9c/YirEmcLl10oKa9iVcZRZqS6pwrnySZ2CSIkKIC5azMbP7gFmjKMs8o1t+4i4Alr7fPGmFsaO8lamwEMr2N7PnB6HdstzgXcRURERER8xuGwzN+aw/TUBEb3PLVHa0DnSJbsPkJ5VTWhQT4couhh1lrW7DvKaakJzT63W6cORIUF+b4iZ+4OCImA6O6NH1uHZel5VFQ73LbkwskiQgznDO7Me+uzeHDWALcPeW1Kz95xY8xDwHXAp8aYAMAzMwhFRERERHxsQ+YxDheVc+7QulcBG5gcRZXDkp5b7OXIvGvPkRLySyqavL5ebcYYBnWJ8n3PXu52SBgAAU1Je061aFcuEaFBjGnmMNbmuGpsd4rKqvh82yG3X7spr/pKoBy4xVp7CGfhlL+5PRIRERERkTZg3pZDhAQG1NubMzA5EsDvh3LWzNcb17tla8sN7hLNzkNFVDt8uCZh7g5IHNCiU621LNp5hCn94gkJalmy2BQT+sTRIzacN1e7fyhno1Fbaw9Zax+11n7jen7AWtuUOXsiIiIiIu2KtZb5W3KYlhJPZFjdg9l6xXUkNCjA74u0rN6bT0JkKL1aWJhkcJcoyiodZBzxUQ9o8REoOdLi4iw7co5zqKjMY0M4awQEGK4c250VGfnsyytx77UbO8AYM8EYs8YYU2yMqTDGVBtjfDz4VkRERETE/TZlFXKwsIxZQ+oewgkQFBhAaudIdhzy32TPWsvqvUcZ1zv2O8VpmsPnRVpaUZzFWstHmw4CML0Fcxab67LR3QgwuL1QS1P6I58ArgbSgA7ArcB/3BqFiIiIiEgbMH9LDsGBhjMGJjV43IDOkezIOY6zxqD/2ZFznIOFZUzq27IhnAB9E5w9oD4r0pJbk+w1r2dvX14JN7+0hqe/3sP01AQSo8I8ENx3JUWFMXNAIm+vy6Ky2uG26zalGifW2nRjTKC1thp40RizAXjIbVGIiIiIiE8Unqhk4Y7D5BSWcaiwjJzCMg4XOb9fNrobD85q2Xyn9shay7ytOUzuF090eMP1CAcmRzF3bRZHjpd7JRnwtg83ZRMUYBrs4WxMUGAAAzpH+q5nL3cHdOgEEQ0n7jVOVFTz5KJ0nl2SQUhQAL86byA3Turl2RhruXJsD77csZZFO3M5a3Bnt1yzKcleqTEmBNhojPkrkEPTegRFREREpI2745V1rMjIByC6QzDJ0WF0jg6jrLKat9Yc4P+dnUpgQMuG8bU32w4WkXn0BD+e0b/RYwd0jgJgx6HjfpfsORyWjzceZFpKArEdQ1p1rUFdopm3JQdrbYuHg7ZY7g5nr14j97XW8vm2Q/zhkx1kF5zgByO78tCsAV5v1xmpCSRGhvLWmky3JXtNSdqucx13N1ACdAcudcvdRURERMRnduQUsSIjn5+c3p/tvz+bTb85i8/uncZLN4/jntP7c6y0ko2ZBb4O02vmbckhMMBw5qDGe4L+V5HT/+btrdl3lIOFZVw0okurrzWmZycKT1SyJdvLQzmtdSV7jc/Xe37pXu54dT2RYUHMvX0ij105wicJfFBgAJeN7saiXbkcOV7ulmvWm+wZY/obYz4E5gEvApHW2t9Za++31qa75e4iIiIi4jNzlu8jLDiAmyf3IjzkuwO+pqUkEBhgWLQz10fReZe1lnlbcpjUN45OTejNigkPITk6zC+TvQ83HaRDcGCTkt7GnDEwiaAAw7wt7l9DrkFFB6G8sNFkr6LKwbNLMpjUN45PfjyFcS1YU9Cdzh7cGYfl29721mqoZ+8F4BOcvXjrgX+75Y4iIiIi4nPHSir4YGM2PxjZlZjwU5Ob6A7BjO7Zia++J8nezkPH2Zdf2qw5agOTo9jpZ2vtVVQ5mLclh7MGJ53yB4CWiA4PZlK/eOZvzfFuMZua4iwJDSd787bkkHu8nNnT+hAU6PuZakO6RhMZFsTy9Dy3XK+hVxRprX3OWrvLWvs3oJdb7igiIiIiPvfW2kzKKh0NFqCYkZrI9pwiDhWWeS8wH5m/JYcAA2cNbnpv1oDOkew5Ukx5VbUHI/OuJbuPUFBaycUjurrtmucO6cz+/FK2e7MXNHe783sDPXvWWl5Ytpe+CR2Z1t/zyys0RWCAYUKfOJbv8XzPXpgxZqQxZpQxZhTQ4aTnIiIiItIOVTssr6zYz/jesd8WGqlLzWLSi3f5f+/evK2HGN87jviI0CafMzA5iiqHJT3XR4uGe8AHG7OJ7RjClP7xbrvmWYM7ExhgmO/NoZy5OyCiM4TXPyxz/YFjbM4q5ObJvQloQ0WIJvWN48DRUjKPlrb6Wg0leznAo8A/XF+Haj3/e6vvLCIiIiI+8eWOw2QXnODmyb0aPC4lKYKuMR38fihn2uHjpOcWc+7Q5lVAHJjsqsjpJ0M5i8ur+HLHYc4bmkywG4c0xnYMYUKf2G+rcnrFkcaLs7ywdB/RHYK5ZJT7ejHdYVJfZ6Ltjnl79baitXZGA18zW31nEREREfGJl5bto0t0WKMLhxtjmJ6awLL0PL8aqniyeVsOYYyzOEZz9IoLJzQogJ1+UqTli22HKKt0uKUK58lmDUkmI6+E3Ye90AvqcEDuzgYXU886Vsr8rTlcPa6HW+YmulNKUgTxESFumbfn+1mIIiIiIuI1uw4dZ0VGPtdP7NWkghQzByRSUlHNmr3HvBCdb8zfmsPYnrHNLrcfFBhAaudIdhzyj2Tvw40H6RrTgVE9Orn92mcP7owxzoIoHnd4C1SdaLBn75UV+zHGcMPEnp6Pp5mMMUzsG8/yPfmt7glVsiciIiLyPTJnxT5CgwK4amz3Jh0/qW88IUEBfjuUM+NIMTsPHWdWM4dw1hjQOZIdOce9W2nSA/KKy1mansdFI7p4ZP5aQmQo43rFMn+rh5O9ggPwxjUQHgd96x6MWFJexRurD3DOkM50ieng2XhaaFLfOHKPl7PnSEmDxzX2707JnoiIiMj3RGFpJe+vz+aiEV2atJYcQIeQQCb2ifPbIi1Ldh8BaHRIa30GJkdxtKTCbYtg+8qnm3OodlgucmMVzpOdOzSZ3YeLSc/10BzHooMw5wKoOA7Xvw/Rdb+W99ZnUVRWxQ8n9/ZMHG4wqW8cAMv3NDyU88/zdza4v6FF1Uc19NWCmEVERETEh+auzeREZXWDyy3UZeaARDLyStiX13AvQ3u0IiOf7rEd6B4b3qLza6qZenVZAQ/4YGM2AzpHkto50mP3OGeIs/fUI1U5i4/AyxdBSR5c9x4kD6/zMIfD8uKyfQzvHsOoHjHuj8NNesSG0zWmA8vT6y/SUlxexRurDjR4nYZ69v7RwJeqcYqIiIi0I9UOy8sr9zGuVyyDu0Q369wZqc4lGPxtKKfDYVmZcZSJfeJafI2Byc7kaOeh9luR80B+KRsOFHi0Vw8gKSqMMT07MW+rm5O90qPwysVQkAnXzIVuY+o99OvdR8jIK+GHk3thTNtZbuFkxhgm9Y1jRUY+DkfdQzXfWZvJ8fKqBq+japwiIiIi3wOLduaSefQENzWy3EJdesSF0zehI4v8bCjn9pwiCk9UMrFvy5O9mPAQkqPD2NGOe/Y+2pQNwIUeqMJ5sllDk9mRU8Red/USlxXCq5dA3m64+nXoNbnBw19YtpekqFDOHZrsnvt70KR+cRSeqKyz19jhsLy0fB8jG+mdbNKcPWPMEGPMFcaYG2q+WhSxiIiIiPjEp1tyiO0YwlmDWjY3beaARFZlHKWkkZ6E9mSlax2ziX1at4D4wOQodrbTtfZKK6qYuzaLcb1i6eqFYiXfDuV0R6GWihLsa1fAoS1wxcv1FmSpsfNQEd+k5XHDxF5uXUfQU2rW26tr3t6iXbnsyy9tdN5ho6/SGPMb4N+urxnAX4ELmx+uiIiIiPiCw2H5Ji2Pqf3jm7TcQl1mDEikotrBMjes/dVWrNiTT5/4jnSObt6SCycbmBzJniPF7W4tQmstv3hvC5nHSvnJGf29cs+uMR0Y0T2m1fP2rLW8v3InezKzWTzkEUid1eDxDoflV+9vJSosiGvG9WjVvb0lKSqMvgkdWb7n1Hl7LyzbS3J02LfJc32a8mm/DDgdOGStvRkYDjRvoLeIiIiIj/3t8508Mn8n2QUnfB2K1+04VERecTlT+ye0+BpjesYSERrEol1H3BiZ71RVO1i19ygTWjGEs8aAzlFUOSxp3lgw3I1eX32ADzYe5L4zUpjcr3W9m81x7tDObMkuJPNoaYvOP5BfyrX/XcV98w5xBX9h9rpubD/Y8DDal1fsY+3+Y/zmgsFNrkTbFkzqG8/qvUeprHZ8u23noSKWpec3qYeyKcneCWutA6gyxkQBuUDTFmYBjDGBxpgNxphPXM97G2NWGWPSjTFvGWNCXNtDXc/TXft7NfUeIiIiIg3ZmFnAk4v28PTXe5j210Xc9do61u472u7XRmuqb9KcvXFT+7f8F/qQoACm9o9n8a5cv3jfth4sori86tsS960xMNlZkbM9FWnZklXI7z7azrSUBO6e0c+r9541xDlfrrlDOasdlheW7uXsfy5hc1Yhf/rBEL742RlEhwdz71sbKKusu2c182gpf/lsF9NTE7hklGeL0LjbpL5xlFZUsymz4NttLy7dR1hwAFePazwla0qyt9YYEwM8B6wD1gMrmhHjT4AdtZ7/BXjMWtsPOAbc4tp+C3DMtf0x13EiIiIirfbU4nSiwoL44r5p3Dq1N0vT8rjs6RVc+MQy3lufRXU91e78xTdpR0hNiiQpqnXDFWekJpJTWMaOdjo/rbYVrqFxE1pRibNGr7hwQoMC2kyRlvTcYt5Zl1XvsNLC0krufG0d8REh/PPKER5ZRL0h3WPDGdo1mk83Nz3ZS88t5vKnl/P7T7YzoU8sX9w3jWvH9yQ+IpS/Xz6c3YeLeaSONeestfz83c0EBhge/sHQNl2Bsy4T+sRhDN8O5cwvLuf9jdlcOqobMeGN91A2muxZa++y1hZYa58GzgRudA3nbJQxphtwHvBf13MDzATecR0yB7jY9fgi13Nc+0837a01REREpM1Jzz3O59sOc+OkXqQkRfLQrIGs/MXp/PHiIZRWVHH/3E08s2SPr8P0mBMV1azZe4xpKa0fpjd9gHMYqD9U5Vy+J4+UpAjiI0Jbfa2gwABSO0eyNbvQDZG13p/n7eBnb2/ijEe/5sON2d8p3e9wWO6fu5HDRWU8ce0oYn00pPGiEV3YlFXYpAS5rLKaq55dQUZeCY9dOZwXbhpLl1rFZE5LSeCmSb14afk+vt793WHGb67JZPmefH5x7sDvnNNedOoYwqDkqG+LtLy+6gAVVQ5ubmJV3aYUaFlY89hau89au7n2tkb8E3gAqBlkGgcUWGtryjhlATV9qV2BTNd9qoBC1/EiIiIiLfb01xmEBQdwU62FxMNDgrhuQk8W3HcaE/rE8taaTL8YmliXVXvzqah2tGq+Xo3EyDCGd4/h820eWBTbiyqqHKzdd6xV6+udbFr/BFbvO0rWsZbNQ3OXsspqlu1xFuOJCA3mJ29u5MInl35bWOeZJRks3JnLL88dyKgenXwW52WjuxESFMBrq/Y3euyHG7PJK67gqWtH84OR3ersnXtw1gBSkiL42dubyC8uB+BgwQn+9OkOJvWNa9KQx7ZqUt841u8voKiskpdX7ue0lAT6JUY26dyg+nYYY8KAcCDeGNMJqHlXo/hfglYvY8z5QK61dp0xZnqTomkCY8xsYDZAQkICixcvdtelpQ0oLi5Wm/oZtan/UZv6H39u0/wTDt5ff4KZPYLYsrbuWShDO1ayMqOC5z74ipROgV6O0DNqt+nrO8oJCoCyzK0sPtj6QVOpHSqYu7uSd+d/RVyHtl++vi5px6o5UVlNVNkhFi92T3XRXg4HWPjL20u5NMX9vWVN/ZxuzauirNLBmKjjDO0TyIr4EN5LO861/11FaqcAdh9zMK5zID0r9rF4ceOJlieNTQzgnTUHmByRR4eguv9tWmt5YnkZ3SIMZQc2sziz/n/D1/Wt5vcryrn12UXcMzKUx9aXU1FVzcVdS/n666899TJapSntGlFaRUW1gzuf/Yojx6sYk1LU5J/Z9SZ7wO3AvUAXnPP0ahQBTzTh2pOBC40x5wJhOJPEfwExxpggV+9dNyDbdXw2zsIvWcaYIJwVP0+pM2qtfRZ4FiA1NdVOnz69CaFIe7F48WLUpv5Fbep/1Kb+x5/b9Hcfb8OY/fz26mn1riE2tryK13Z9SYYjgdnTh3k5Qs+o3aZ/Wv81E/uGcdbp491y7V5DSpj798UURPbm0ikNr/HVVm1emIYxu7nlgmlNmvfUVPMPr2FFViGP3jLN7eu4NfVz+vXH2wgNOsDsi2bQISSQmcBPK6t5ecU+nly0hz4JHXjhzilEhDaUBnhHVJ9jXPKf5eRH9OG6CT3rPGZVRj6Zn6/kkUuGMqMJSyaUx2Twp3k7eD0zks1HSvnNBYO4opG16HypKe06pryKf2/4gmUHq+iXGMHdl01r8tzDev8VWmv/Za3tDfzMWtu71tdwa22jyZ619iFrbTdrbS/gKuAra+21wCKcyzkA3Ah86Hr8kes5rv1fWX8dTyEiIiIed7SkgjdXZ3LRiK4NLhbdMTSIWUOS+XRzTr3V/NqrnMITpOUWt6oK58l6xXdkQOdIPt/afodyrtiTz8DOUW5N9ACundCDvOJyvth22K3XbY6vdx1hQp84OoT8r5c6LDiQ2dP6suKhmXz847aR6AGM7B7DwOQoXlt1oN5h1HNW7CO6QzAXjWhaFc1bpvRmcr84Fmw/zJienbhxYi83RuwbEaFBDO8eA8DNk3s1q8hMU/7k8Iwx5h5jzDuur7uNMcEtCxWAnwP3G2PScc7Je961/XkgzrX9fuDBVtxDREREvudeWr6PE5XV3HFan0aPvXR0V46XV7X7uWgnq1lyYVpK6+fr1XbOkM6s2X+U3ONlbr2uN5RVVrPuwDG3LLlwstNSEuka06FJ89A8YX9+CRl5JcxIrbu9w0OCCA9pG4kegDGG6yb0YEdOEesPFJyy/2DBCT7fdpirxnX/TvLakIAAwz8uH8ElI7vy98uHe73SqKecM7gzydFhXDKyW7POa0qy9x9gtOt7zeOnmnMTa+1ia+35rscZ1tpx1tp+1trLrbXlru1lruf9XPszmvVKRERERFyKy6uYs3wfZw1Kon9S44UMJvSOo2tMB95dn93ose3Jkt1HSIgMJbUJ70FzzBqSjLWwYLvverBaav2BY1RUOZjogWQvMMBwzfgeLN+Tz54j3l9gfbFrwfvpqYlev3dLXTSiKx1DAutMkF9duR9rLdfXM8SzPp2jw3j0yhH0iu/orjB97rZpfVj685lNTnpr1JvsuebNAYy11t5orf3K9XUzMLYVsYqIiIh41JurD1B4opI7p/dt0vEBAYZLRnVladoRDhe1v96qulQ7LEvTnVUZ3b2aVUpSBL3jO/JZOxzKuXJPPgEGxvaO9cj1rxjTnaAAw+urDnjk+g1ZtCuX3vEd21WSExEaxA9GdeWTzTkcK6n4dntZZTVvrD7AGQOT6NYp3IcRth2BLeilbKhnb7Xre7Ux5tuflMaYPoB/DWgXERERv1FeVc1z32QwsU8cI5tRWv6SUd1wWHh/g3/07m07WEhBaSXT3LDkwsmMMZwzpDMr9uRTUFrR+AltyPI9+QztGk1UWGtmJdUvITKUs4d05p11WV6dA1pWWc2KPfmc5uYhu95w7fieVFQ5eHd91rfbPtp0kGOlldzUxPXkpG4NJXs1qePPgEXGmMXGmMXAV8BPPR2YiIiISEt8sCGbw0Xl3DWjab16NXrHd2R0z068uy7LL9bcq5mvN8WNxVlqO2dwZ6ocli93tJ8F1ksrqtiUVcDEvp55T2pcN74nhScq+WRzjkfvU9uKjHzKqxzMGNB+hnDWGJgcxeienXht1QEcDou1ljnL95GaFOnWtRC/jxpK9hKMMfcDI4BncCZ5XwHPASM9H5qIiIhI881Zvp9ByVFM6df8X+gvHdWNtNxitmQXeiAy71qy+wiDu0QRHxHqkesP6xZNl+gwPtvqvYSmtdbuO0ZltfXIfL3aJvSJpW9CR15d6b1CLV/vOkJYcADjPTQ81dOuHd+DvXklrMjIZ93+Y2w7WMQNk3q6fQjy901DyV4gEAFE4lyPz7i+glzbRERERNqUfXklbM8p4pJRXVv0S+J5w5IJCQrg3XVZjR/chp2osqzbf4ypHhjCWcMYw9lDOrMkLY/i8iqP3cedlu/JJyjAMKZn04f3toQxhmvH92RjZgFbvfSHg0W7cpnUN56w4OYV8Ggrzh2aTEx4MK+u3M+Ly/cRFRbED0Y2bbkFqV9DyV6Otfb31trf1fXltQhFRESkWfxtrbjmmO8qGDJraHKLzo/uEMxZg5L4aNNBKqoc7gzNq3YerabKYZnmoSGcNc4Z3JmKKgeLdraPoZwrMvIZ0T2Gjl5YZ+7SUd0ICw7gNS8UatmbV8L+/FKm17PkQnsQFhzI5aO78cX2w3y+9RBXju3eppaJaK+aMmdPRERE2on9+SWM/P0Cfvn+Fqod7X/eWXPN35rD8O4xDS6i3phLR3fjWGklX7WTBKYuW/OqCQsOYHQvz/ZgjekVS3xECJ/5aH3CFXucQ/6aYteh42zNLvT4EM4a0eHBXDCsCx9uzOZ4WaVH71WTbE9PaX/z9Wq7ZnxPqh2Wamu5wQ8WQ28LGkr2TvdaFCIiIuIWb6/N4kRlNa+tOsA9b25o171TzZV5tJTNWYWcO6Rzq64ztV88CZGh36kM2N5sy6tmQp84QoM8O6QvMMBw5qDOLNqZ6/Ue5eNlldw6Zw2XPb2cxxem4WjgjxtfbDvEJf9ZRmzHEC4d1bxFqVvjugk9Ka2o5oNWVnhdf+AYf151gs1ZBXXuX7z7CH0SOtIjrn0vUdA7viMXDu/CZaO60T22fb+WtqLeZM9ae9SbgYiIiEjrVDss767PYnpqAr84dwCfbs7hljlrKK1oH/OpWqtmzbdZQ1o2hLNGUGAAPxjZlUU7c8kvLndHaK1ireW/32Tw5fbDDSY0NQ7kl3Ko1HpkyYW6zBrSmdKK6m+rf3rL+xuyKamoZkq/eB5dsJubXlrD0ZLvLgNhreXxhWnMfmUd/RIj+PjuKV5dg25Yt2gGd4nirbWZLb7G4aIybn9lHbuOObj2v6vYcOC7PZknKqpZmZHPjHa0kHpDHr96JH+7fLivw/AbDfXsiYiISDuyYk8+OYVlXDa6G7On9eWvlw1jWXoe1/53VbtbC60l5m3NYUjXKLf0blw6qhtVDstHmw66IbLWWX+ggD9+uoNbX17LjH8s5sVle08piGKtZd3+ozz47mbOffwbAgxem781oU8cUWFBzPdiVU5rLa+s2M+wbtG8/MNxPPyDoazMyOe8x7/5dlhnaUUVP3p9PY8u2M0PRnblrdsn0jk6zGsxgrNQy+Wju7E1u4gdOUXNPr+8qpo7X11HSXkV948OJbZjCNc/v5q1+/7XJ7MiI4+KKke7nq8nnqNkT0RExE+8sy6TqLAgzhiYBMAVY7rzn2tHsy27iCufWcnhojIfR+g5BwtOsOFAQat79Wqkdo5kSNeoNjGUc96WHEICA/jH5cOJjwjldx9vZ8LDC/n9x9vZnFXA01/v4fRHv+bSp1bw4caDnDOkM78YH0afhAivxBcSFMAZg5L4cvthKqu9M2x4ZcZR0nKLuW6CszT/NeN78N6dkwgODODKZ1bw+MI0Ln1qBZ9tPcQvzh3Ao1cM91mVygtHdCU40LSowuvvPt7O+gMF/P3y4QxLCOKt2RNJiAzlhhdWsyojH4DFu47QITiQce10yQXxLCV7IiIifqCorJLPth3iwhFdvvNL7TlDOvPSzWPJOlbKpU8tJ68NDEv0hP8N4WzdfL3aLh3l7JHZdei4267ZXA6HZd6WHKalxHPp6G68e+ckPvzRZM4YmMjLK/Zx4RPLeGT+TmLDQ/jrpcNY86sz+Pvlw+kX493E5pzBnSkqq2L5nnyv3O+VlfuICQ/mwuFdvt02pGs0H/94CjMHJPLogt1kHSvlhZvGMntaX5+u1RbbMYTTByTxwcbsZiXDb6w+wOurDnDX9L6c66ou2zk6jLdmTyA5OoybXlzD8vQ8Fu3KZXI/z8/PlPZJyZ6IiIgfmLc5h7JKB5eN7n7Kvkn94nn11vHkFJbxxFfpPojO8+ZvzWFA50i39mZdOLwLQQHGp717GzKPkVNYxnnD/tdjObx7DP+8aiTLHpzJXy4dysKfnsY7d07iirHdifDCkgJ1mZaSQEx4MK+v8vwi4ocKy/h822GuGNP9lN666A7BPHP9aJ68ZhSf/HgK09vIPLbLRncjr7iCr3cdadLx6/Yf49cfbuW0lAR+elbqd/YlRoXx5uyJdI/twE0vriHz6Ik28zql7VGyJyIi4gfeWZdFv8QIhneLrnP/yB6duGJMN15fdYCsY6Vejs6zDheVsXb/sW97P9wlLiKUGQMSeX9DNlVeGp54sk83H3IOk3QNza0tKSqMK8f2oK+Xhms2JCw4kGvG9eCL7Yc5kN/8f1/WWvbnl/DBhuxG50m+vvoADmu5bnzPOvcbYzhvWDI947xXiKUxp6UmEB8RwjtNGMqZW1TGna+uIzm6A49fNZLAgFN7JRMiQ3njtgn0SeiI8eL8TGl/tFKhiIhIO7c3r4S1+4/x4KwBDQ5X+/HM/ry7Ppt/fZnmV9XuPt92CGvh3KHuG8JZ49JR3Viw/TDfpOd5vdrht0M4+ycQGRbs1Xu3xPUTe/LskgzmrNjH/50/qMFjrbXfrpG3IbOAjZkF36mkWVJexdXjepxyXkWVgzdWH2B6SkK7WmYgODCAi0d0Zc6KfRwtqSC2Y0idx1VWO7jztfUUl1fx8i3jiA6vv93jIkJ5+46J7M0roVun9vNeiHepZ09ERKSde3ddFgEGfjCya4PHdYnpwPUTevLu+izSc4u9FJ3nzduSQ//ECPolRrr92jMHJNIpPLhJPTLutiHzGIeKyjh/mHt7LD0lOboD5w5N5q01mY0uIv7EV+lc899V/GPBbg4cLeX0AYk8/IOhfHrPFKalJPB/H2xlRR3z/z7fdogjx8vb5YLbl47uRmW15aON9a+59++v0lm3/xh/uXQYAzpHNXrNyLBghnWLcWOU4m+U7ImIiLRjDoflvfVZTEtJICmq8bLyd03vS4fgQB5bsNsL0XnekePlrN57lFluHsJZIyQogAuHd2HB9sMUljacwLjbJ5tzCAkK4PSB7Wc+1g+n9Ka4vKrB5DjzaClPLErn7MFJbPrNWXx5/2n87fLhXDO+B4O7RPPENSPpFd+RO19bx768ku+c+8qK/XSP7cBpKe1v2OLA5CiGdI3inXrmgG7OKuDJRelcMqorF9QqPCPSGkr2RERE2rEVGfkcdK2t1xRxEaHcMrUPn27JYWt2oYej87wvth/C4aEhnDUuHd2NiioHn2zx3pp7Dodl/pZDnJbSPoZw1hjRPYZRPWJ4afk+qutZAP6Pn24nwBh+c8Fgojuc+tqiwoJ5/sYxANwyZw1Frl7CnYeKWL3vKNeN70lAHfPY2oPLRtW95l5ZZTX3z91EQkQov7lgsI+iE3+kZE9ERKQde2dd1nfW1muKW6f2JiY8mL99vsuDkXnH/C2H6BPfkdQk9w/hrDG0azT9EyNatE5aS60/4BzCeZ6Heiw96YdTerM/v5Svduaesm/J7iN8vu0wd8/sR5eYDvVeo2dcR56+bjT780u5+/UNVFU7eHnFfkKDArhizKkVZ9uL+tbce3TBbtJzi/nrZcPqTIBFWkrJnoiISDt1vKyS+VtzTllbrzFRYcHcNb0vX+8+8u3CzO3R0ZIKVmTkM2toZ4+uo2aM4dLR3Vh/oICMI96Z6/jplvY3hLPGOYM70yU6jBeX7f3O9ooqB7/9eBu94sK5dWrvRq8zoU8cf7x4CEt2H+GX72/lgw3ZXDC8C53qKW7SHtS15t6afUd57psMrh3fg2ntcHiqtG1K9kRERNqpeVvqX1uvMTdM7EVSVCh/+3wX1tY93M4XDheV1Tv8r7aC0gqeX5pBtcMya4jne79+MLIrAQbeW19/cQ13qanC2d6GcNYICgzghkm9WL4n/zvDFV9ctpeMIyX85oLBTV4A/KpxPbhlSm/eWptJaUU1N0yse7mF9qT2mnsl5VX8dO4muncK5xfnDvR1aOKHtPSCiIhIO9XY2noNCQsO5Mcz+/OrD7ayOT6UGR6Ir7nmb8nhztfWExEaxPDu0Yzs3okR3WMY0SOGqLBg1h84xjdpR1ialsfm7EKshVE9YhjcpfGqha2VFBXGlP4JvL8hm/vPTGnVnLHKagefbD5ITIcQZgw4tedu3YFjHC4qbzdVOOty1dju/OvLNF5ctpe/Xjacw0VlPL4wjTMGJtb5mhvyi3MHcriojBMV1X5RebL2mnuLd+eSeayUt2ZPpGOofi0X99O/KhERkXYoPfc4a/Y1vrZeQ64Y051nl2Twzu4yfuywPi16Ya3lycXp9IgN57SUBDZkHuOpr/d828sXEhhARbWDwADDyO4x3Ht6ClP6xzO8W7RHh3DWdumorvzkzY2szMhnUr/4Zp9fXlXNO+uyeGrxHrKOnQDgh5N78+CsAYQE/W+w1affVuFs+jzMtiYmPIRLR3dl7tosHjhnAH+et4NKh210/b26BAYYnrhmVJvqgW6NmjX3XnQVsbltam/G9Y71dVjipzyW7BljwoAlQKjrPu9Ya39jjOkNvAnEAeuA6621FcaYUOBlYDSQD1xprd3nqfhERETas1dXHiAkMIDLm1iFsy4hQQHce0Z/7p+7iQU7DnP2YM9VtGzM8j35bM0u4pFLhnKVazHtExXVbMkuZGOms6drXO9YJvaNI8pHQxvPHtyZyNAg3lmf1axkr6yymrfWZPL013vIKSxjRPcYfnvBYJam5/HCsr1syDzGk9eMoktMB2cVzq05TE9JIKKd9/TcNKk3r648wM/f2czCnbn8eGY/esZ1bPH1vJXUe8Olo7vx36V76ZcYwU/PSvV1OOLHPPlTpByYaa0tNsYEA0uNMfOB+4HHrLVvGmOeBm4BnnJ9P2at7WeMuQr4C3ClB+MTERFpl05UVPPu+ixmDe1MXERoq6514fAuPPLJZp74Kp2zBiX57BfqZ5ZkEB8RysW1FobvEBLIuN6xbabXIyw4kPOGJfPRpoP8/qKqJiVji3bm8sC7mzlyvJyxvTrx18uGMaVfPMYYzhiUxNhesfz83c2c9/g3PHblCDqGBnG4qJzz2vEQzhr9EiOYnprAwp25dI3pwF3T+/k6pDZjYHIUf7hoMBP7xjeruJJIc3msQIt1qilZFez6ssBM4B3X9jnAxa7HF7me49p/uvGnP+GIiIhblVVWs3DHYcoqq30ditd9vOkgx8uquHZ864tVBAUGcF6fYLZkF7IkLc8N0TXf9oNFLNl9hJsn92rzv/hePa4HpRXVvHRSpcm6lFZU8cC7m4npEMwbt01g7u0Tmdo/4TsJ9XnDkvno7skkRYVx80tr+Pm7m9v9EM7aZk/tgzHwf+cPokNI225bb7t+Yi/6JUb4Ogzxcx6txmmMCTTGbARygQXAHqDAWlvlOiQLqPkTXlcgE8C1vxDnUE8REZFTPPFVOrfMWctpf1vEC0v3cqLi+5P0vbpqPylJEYzt1ckt15vcJYgu0WH8e2GaT+ZFPfdNBh1DArnODcmrpw3vHsMZA5N4ZkkGBaUVDR47Z/l+jhwv5+FLhjKxb1y9vaZ9EiJ4/67JXDaqGxlHSvxiCGeNSf3iWf+rMzlniO+GCIt8nxlv/FA3xsQA7wP/B7xkre3n2t4dmG+tHWKM2QqcY63Ncu3bA4y31uaddK3ZwGyAhISE0XPnzvV4/OI9xcXFRETor1z+RG3qf9pCm5ZUWn72dSk9IgMwBnYedRAVArN6hzCjexBhQf47MGRvYTW/W1HGdQNDOKOne+auFRcXszI/lFd3VPDguDAGxHqvBybvhIMHlpzgzB5BXD2wdUNSvSXzuINfLzvBub2DuTy17jXfSiotDywppW9MIPePDmvytbflVdMlwtAprHV/j28Ln1NxL7Wpf3JHu86YMWOdtXZMXfu88mcja22BMWYRMBGIMcYEuXrvugE1C9ZkA92BLGNMEBCNs1DLydd6FngWIDU11U6fPt0Lr0C8ZfHixahN/Yva1P+0hTb998I0TlTt5tHrJzG4SzSr9x7l31+l8dauPL7ItPzk9P7cNLnxRZvbo/nvbKZD8EEeuHK62wqVLF68mF+dPZXP/rKIpUcjuOOS8W65blP8/uPtBJh9/PrqaXSN6eC1+7bW2pINfL7tEL+5egKJUacmc3//fBcllen8+eqJDO7S9KUxprspvrbwORX3Upv6J0+3q8eGcRpjElw9ehhjOgBnAjuARcBlrsNuBD50Pf7I9RzX/q+sv9TYFRERtykpr+L5ZXs5fUDit79Ej+sdyyu3jOe9uyYxqEsUv/14O2v2HfVxpO5XeKKSjzYd5KIRXdxekTIsOJDZ03qzND2PDQeOufXa9SksreTNNQe4YHiXdpXoAdx3RgpV1ZYnFqWfsu/I8XJeWLaX84clNyvRExFxN0/O2UsGFhljNgNrgAXW2k+AnwP3G2PScc7Je951/PNAnGv7/cCDHoxNRETaqddW7aegtJIfzTy1st+oHp347w1jSYwM5ZH5O/1mXa4a76/P4kRltVsKs9Tl2vE9iQkP5sk6EhhPeHXVfkorqpk9rY9X7udOveI7csXY7ryx+gCZR0u/s+/JRemUVzm4/8wUH0UnIuLkyWqcm621I621w6y1Q6y1v3dtz7DWjrPW9rPWXm6tLXdtL3M97+fan+Gp2EREpH0qq6zmuW/2MqVfPKN61F2cpENIIPedmcK6/cf4YvthL0foOdZaXlt1gOHdohnazTO9RR1Dg7hlcm++3JHLtoOFrbpWRZWDRTtz+e1H23hlxT4KSyu/s7+sspoXl+3jtJQEBiZHtepevnLPzP4EGMM/v0z7dlvWsVJeX3WAy0d3o0+C5leJiG95tBqniIiIO81dm8mR4+XcXUevXm2Xj+5G34SO/PWznVRVO7wUnWet3nuUtNxij/Xq1bhhUi8iQ4P4z6I9zT63rLKaBdsPc/9bGxn9xwXc/NIaXl99gP/7cBtjH/6SH7+xgSW7j1DtsLy/IZu84nJub4e9ejU6R4dx46RevL8hi7TDxwH415dpYOCe0/v7ODoRES8VaBEREWmtiioHTy/ew5ienRjfyCLbQYEBPHDOAG5/ZR3vrMviqnE9vBSl57y66gCRYUFcMLyLR+8T3SGYGyb15D+L95CeW9zkdcAe/WIXzy/dS0lFNdEdgjlncGfOHZrMpH5xpB0u5u21mXyw8SAfbzpIl+gwqhyWoV2jmdi3fa+ydMdpfXl91QH+8cVufnZ2Ku+uz+Lmyb3p0s7mIIqIf1KyJyIi7cL7G7I4WFjGw5cMrXe9strOGpTEqB4xPPblbi4a0bVdL+icV1zOZ1tzuHZ8T6+8jh9O7s0LS/fxn0XpPHrliEaPT889zuNfpTNzQCI3T+7FhD5xBAf+b/DQkK7RDOkazUPnDuTLHYd5e20WS9Pz+OPFQ5rUlm1ZbMcQbpvah8e+3E12wQk6BAdy1/S+vg5LRATQME4RkXbHWsvKjHx+9Np6/vnlbl+H4xVV1Q6eWryHoV2jOS0loUnnGGN46NyBHC5yVkZsb6odlsITlWQXnOCFpXuprLZcN8E7PZRxEaFcO74HH246yP78kkaP/+83ewkLDuDvlw9nav+E7yR6tYUFB3L+sC7M+eE4dv7hHM4a7B8Lbd8ytTexHUPYkl3ILVP7EBfRPtYLFBH/p549EZF2oqyymo83HeSFZfvYkVNEYIBh/lbLeUOT6Z8U6evwPOrTLTnsyy/lmetHN6snaGyvWM4YmMTTi/dwzbgedOpY9wLY3lZZ7WBlRj45BWXkFJZxqKiMQ4UnyCks42hJBcXlVZRWVH/nnAl9YumX6L12nn1aH15ZuZ8nvkrnb5cPr/e4vOJy3tuQzeWjuxHbjPe3voSwPYoIDeL/nZ3Kc0syuHWqf67vKCLtk5I9EZE27lhJBS8u38drK/eTX1JBSlIEj1wylKkpCZz16Nc8umA3T1032tdheozDYXniq3RSkyI5c2BSs89/4JxUzvnnEp5YlM7/nT/IAxE238/f3cx767O/fR4fEUJSVBjdOnVgeLcYIsOCiAgLIiI0iMiwIDqGBjGukXmK7pYYGcY143vw8or9/Hhmf3rEhdd53Csr9lNR5eCHU77fSc7V43pwtR/MDRUR/6JkT0SkDSsqq+SqZ1ey6/BxTh+QyA+n9GZS37hve7dundqHfy1MY3NWAcO6xfg2WA95f0M2abnF/OuqEQQENH9+V0pSJJeN7sYrK/Zz06RedI+tO2nxlq3Zhby3PpsbJvbktql9SIwKJTSobc4nvOO0vry26gBPLErjr5ed2rtXVlnNKyv3c8bARPpqmQERkTbHf8ZQiIj4mYoqB3e+uo49R4p55ZZxPH/TWCb3i//OMMZbp/amU3gwf/t8lw8j9ZyC0goenreDUT1iuGBYy6tQ3ntGCsbAP77w/fv01893ERMezE/PSqV7bHibTfQAkqLCuGZcD95bn33KwuEA763P5mhJBbdObb/LJ4iI+DMleyIibZC1lgff28yy9HweuXQYU/vXXZQkMiyYu6b345u0PFbsyfdylJ731893UXCikj/9YGiLevVqdInpwC1TevPBxoOs3nvUjRE2z7L0PJbsPsLdM/oR3SHYZ3E0xx2n9SXAGJ5clP6d7Q6H5fmlGQzpGtXoUhgiIuIbSvZERNqgf36Zxnvrs7nvjBQuG92twWOvn9iTpKhQ/vb5Tqy1XorQ8zYcOMYbqw9w06ReDEyOavX17p7Zj64xHfjVB1uo9MFC6w6H5ZH5O+ka04HrJnh2YXR36hwdxtXjuvPOuqzv9O4t3p3LniMl3Da1T7tfPkFExF8p2RMRaWPmrs3kXwvTuHx0N+45vV+jx4cFB3LP6f1Zf6CAr3bmeiFCz6uqdvCrD7aSFBnGfWemuOWa4SFB/PbCwew+XMwLS72/FMOnW3LYkl3I/WemEBbcdodu1uWO6c7evf8s/l/v3nNL9pIcHca5Q5N9GJmIiDREyZ6ISBuyZPcRfvHeFqb2j2/y4uEAV4zpTs+4cP72+S4cjvbfu/fKyv1sO1jEry8YRESo+2qJnTkoiTMGJvHPL9PILjjhtus2pqLKwd+/2MWAzpFcPLKr1+7rLsnRHbhybHfeXptF1rFStmYXsiIjn5sm9fKrJRRERPyNfkKLSJuXcaSY8qrqxg9s5zYcOMZdr62nX2IE/7l2VLN+iQ4ODOD+M1PYeeg4H28+6MEoPe9wURn/+GI3p6UkMGuI+xfd/u2FzuUXfvfRNrdd0+GwpOcep6qe4aFvrjnA/vxSfj5rAIGtmHvoS3dO74sx8J/Fe3h+6V46hgRylZYaEBFp05TsiUibVVRWyUPvbWHmP77ml+9v9XU4HmOt5eUV+7jymZXEhAfz0s3jiAxrfvGOC4Z1YUDnSB5bsNsnc9Lc5Q+fbKei2sHvLhzskblg3TqFc8/p/fli+2EW7jjc6usdLirjhhdWc8ajSzjj0a95e23md97/4vIq/vVlGhP6xDI9pe5CO+1Bl5gOXDGmO2+vzeTjTQe5cmyPdlNkRkTk+0rJnoi0SV/tPMxZjy7hrTUHGNI1infXZ7E1u9DXYdVr9d6jfLY1h6VpeWzMLCA9t5jDRWWUVlQ1eF5xeRX3vLmRX3+4jSn94/nkx1PoHB3WohgCAgw/OyuVffmlvLUms0XX8LVv0o7wyeYcfjS9H73iO3rsPrdM6U3/xAh+89E2TlS0vNf4s605nP3PJazdf5Qfz+xHeEgQ/++dzcz4+2JeX3WAiioHzy3JIL+kgodmDWz3hUzumuGcQ+qwlpsn9/JtMCIi0igtqi4ibcqxkgp+/8l23t+QTUpSBE9fP5k+CR057a+L+NOnO3j9tvFt7hfmPUeKufLZFdRXCHNE9xjOHdqZWUOSv7Og965Dx7nztXXsyyvhgXNSuWNa31YtLwBw+sBEJvSJ5ZH5OzktJcHnC4jfOmctuw8f54aJPblibHei6umxrKhysHDHYf40bwe94zty+2meXbctJCiAP1w8hKueXcm/v0rjgXMGNOv8kvIqfvfxNuauzWJo12j+edUI+iZEcP+ZKXy1M5fHF6bxi/e38MRXaRScqOS8ockM7x7jmRfjRV1jOnD/mamcqKjy+b8tERFpnJI9EWkz5m3J4dcfbqWgtJJ7Tu/Pj2b0/XbB6XvPSOE3H23jq525nD4wyceRftdzSzIICQzgtVvH47BQXF5JcXk1xWVVHC4q46uduTw8bycPz9vJkK5RzBqSTGRYEA/P20FkWDCv3zaBCX3i3BKLMYa/XTacc//1Dfe9tZE3Z08gyEcFNDKOFPPljsN0jgrjj5/u4LEFu7lsdDdumtyb3q5eu+0Hi5i7NpMPN2ZzrLSSzlFh/PPKYV6pVjmhTxyXjOrKc99kcMmorvRLjGzSeRszC7j3zQ3sP1rKXdP7cu8ZKYQEOd9jYwynD0xi5oBElqTl8fhCZ7L3s7NTPflSvOrO6X19HYKIiDSRkj0R8bnc42X8+oNtfLbtEEO6RvHyD8czqMt311W7ZnwP5izfx8PzdjAtJaHNVADMPV7Ge+uzuWJsN8b0qnth6fvOTCHzaCmfbT3EvK05/O3zXQBM6BPL41ePJDGyZcM269M9NpzfXzyY+97axFOL9/Dj0/u79fpN9dbaTAIDDB/dPZnDReW8uGwvr68+wMsr93NaSgJHjpez7WARIYEBnDk4iSvGdGdKv3ivFjD5xbkDWbgjlwff3cJbt09s9N6bMgu4/OnlJEaG8eZtExhfT5JujOG0lASm9Y+notrx7R8tREREvEnJnoj4jLWWd9dn84dPtnOispqfnzOA26b2rrMnKjgwgAdnDWD2K+t4c00m17eRRalfWraPSoeDW6c0POywe2w4t03rw23T+nCw4ATpucVM6hvnsV63i0d05audR/jnwjSmpiQwwstDCCurHby7LovTBySSGBVGYlQYj145ggfPHcCrKw/w1poDxEeE8rsLB3PRiC7EhId4Nb4a8RGh/PbCQa7EOJ27Z9afGJeUV3HvWxtJiAjlkx9PoVPHxmM2xijRExERn1GyJyI+kV1wgl+8t4Wvdx9hTM9O/OWyYfRNiGjwnDMHJTG+dyz/XLCbi0d0aVHFSncqLq/ilZX7mTWkc7OKiXSJ6UCXmA4ejMyZZPzx4iGs23eUe9/cwKf3TKWjG9era8zCHbnkFVdw1bju39meGBnG/WemcL+bFkp3h28T4y/TmNo/od65dX/8dAf78kt4/dYJTUr0REREfK1tjIMSke+V+VtyOOvRr1mz7yi/vWAQc2+f2GiiB84E5pfnDSS/pIKnFu/xQqQNe3P1AY6XVTF7WtucwxTdIZhHrxzB/qOl/OGT7V6995trDtA5Koxp/dv+UgM1iXFiZCj3vrWRkvJTK6h+se0Qb6w+wOxpfZjY1z3zK0VERDxNyZ6IeFXhiUoefG8LfRMj+Pzeadw0uXezKlAO6xbDD0Z25fmle8kuOOHBSBtWWe3ghaV7Gd871utDJJtjQp84bp/WlzfXZPLZ1kNeuefBghN8vfsIl4/p5rPiMM1Vkxjvyy/hj59+NzHOPV7Gg+9tYXCXKH56pv8UWhEREf/XPv4XFhG/8dySDApPVPLnS4a2uHR7TWXDv7sKnfjCJ5sPcrCwzONLBLjD/WemMKRrFA+9t5nDRWUev9/ba7OwFq4Y073xg9uQmsT4jdWZfL7NmRhba/l/b2+mpLyKf1014tuqmyIiIu2Bx/7XMsZ0N8YsMsZsN8ZsM8b8xLU91hizwBiT5vreybXdGGMeN8akG2M2G2NGeSo2EfGNI8fLeWHZXs4flszgLtEtvk7XmA7cMqU372/IZvmePDdG2DTWWp75OoOUpAimpyR6/f7NFRIUwD+vHElpRTW/9/BwzmqHZe7aTKb0i2+X67Ddf2YKg7tE8eC7m8ktKmPO8n18vfsIvzpvYJOXZhAREWkrPPknyirgp9baQcAE4EfGmEHAg8BCa21/YKHrOcAsoL/razbwlAdjExEfeHJROuVVDrcU57hzel/6JHTk1jlrWbEn3w3RNd2StDx2HjrObVP7tHoRdG/plxjBXdP78enmHJantzxBPl5WSUllPavHA8vS88guOMGVY9tXr16NkKAA/nXVCEorqrn91XX8ef5OZg5I5Lo2Uv1VRESkOTyW7Flrc6y1612PjwM7gK7ARcAc12FzgItdjy8CXrZOK4EYY0yyp+ITEe/KOlbK66sOcPnobvRpQjGWxkSGBfPm7Al0jenAzS+tZmma93r4nvl6D0lRoVw0oqvX7ukOt5/Wh+6xHfjNR9uorHY0eKy1lg0HjvHG6gP8/uPtXP/8Kib+eSFDf/sFP1lUysebDtZ53ltrMokJD+aswW1r4fvm6JcYya/OG8iGAwVEhAbxl0uHYUz7SOpFRERq80odbmNML2AksApIstbmuHYdAmp+I+gKZNY6Lcu1LQcRP1JZ7eC/3+ytc+5UWHAgN0/uRVKUexfZbgv+9WUaGLjHjQt8J0aG8cbsCVz331XcMmcNz94whtNSPFv9cUtWIcv35PPQrAHtbv5WWHAg/3feIGa/so6XV+znlim96z324Xk7eO6bvQB0CA6kX2IEE/vE0S8pgg9WpfHjNzaQdewEd5zW59tEKL+4nC+2H+L6Cb3a/dpy103oyfHyKsb3jiMhMtTX4YiIiLSIsbb+4ThuuYExEcDXwJ+ste8ZYwqstTG19h+z1nYyxnwCPGKtXeravhD4ubV27UnXm41zmCcJCQmj586d69H4xbuKi4uJiGh9r09bZa3lxW0VLMmqIjwITu4sKKuCyBDDPaNC6RPdvn9ZrlFcXEwR4fxy6QnO6hnE1QPd/4vz8QrL39aUcbDYwd0jQxmR2Ly/Y1U6LAVllqNllmNllmPllqNlDspOrcDPviIHeScc/OO0cMKD219vj7WWR9eVk15QzZ+ndiAm9NSEdcH+Sl7bUcGM7kGc1yeY2DBDQK1/rMeKinkjI4jVh6qZ0T2I6waGEBhgmL+3krd2VfCnyR3oGtm+EuHvO3//2ft9pDb1P2pT/+SOdp0xY8Y6a+2YuvZ5tGfPGBMMvAu8Zq19z7X5sDEm2Vqb4xqmmevang3UnuTRzbXtO6y1zwLPAqSmptrp06d7KnzxgcWLF+PPbfrvhWksydrNPTP7cf9Zp5Zw35FTxG0vr+WRNeX85dKh/GBkNx9EWb+0w8d5ecV+rp3QgwGdo5p0zuLFi5mXHUl4SAUPXz+duAjP9JJMnVLB9c+v5slNRTxxzRDOHty5Sef9ed4Onv0mg5P/7tUxJJDIsOBTEnIC4Kdn9+bcqW2/Cmd9eg4p5ux/LuGbwjj+ccXw7+z7YtshXv98HWcNSuKp60YTWMecxMWLF/PmPafx18938fTXeyC8E/++ZhR/WLeUUT3CufaCyd56KeIm/v6z9/tIbep/1Kb+ydPt6rFkzzjH9TwP7LDWPlpr10fAjcAjru8f1tp+tzHmTWA8UFhruKdIu/fuuiz+sWA3l4zsyn31FCgZmBzFR3dP4a7X1nHfW5vYkXOcn58zoM5fuL3JWsvLK/bz8LwdlFc5eHPNAX40ox93Te/X6FDGfYXVzNtyiHtO7++xRA8gJjyEV28dz40vrOZHr63nyWtHNZrwvbxiH88syeDC4V2Y0j+ezlFhJEeH0Tk6jMiwYI/F6mt9EiK4dWofnlq8h2vG92B0z04AbDhwjHve3MDwbjH866qRDf67CwgwPDhrAN1jO/B/H2xl1r+WkHn0BH+9dJi3XoaIiIg0wpPjbCYD1wMzjTEbXV/n4kzyzjTGpAFnuJ4DzAMygHTgOeAuD8Ym4lXL0vP4+bubmdQ3jkcaKfYQ2zGEV24Zz/UTevLskgx++NIaCk9UejHa7zpyvJwfvrSG33y0jYl94/j83mmcOzSZf36ZxoVPLGVzVkGD57+bVklMeDC3Ta1/fpi7RHcI5pVbxjGkazR3v76eRbty6z32m7Qj/O7j7ZwxMJHHrhzBFWO6My0lgf5JkX6d6NW4e0Y/OkeF8ZuPtlLtsOzPL+HWOWtJigrj+RvH0CGkacOIrx3fk+dvHEt+cQUdQwI5b5jqaomIiLQVHuvZc829q+832tPrON4CP/JUPCK+suvQce54ZR19EyJ4+vrRTSrqERwYwB8uHsLA5Ch+/eFWfvDkMt68fQKJkd4t3LJwx2EeeGczxeVV/P6iwVw/oSfGGP511UguGNaFX36whYufXMZt0/pw3xkphAYFcKy0kkOFZRwqOsGOnONsyavmF+f291oCFRkWzJybx3HNf1dyxyvrePGmsUzqF/+dY/YcKeau19bTPzGCfzbSg+WvOoYG8YvzBnLPGxt4+us9vLMuC4e1vHjT2Gb3wM4YkMgnP55CUVkVHUO9UvdLREREmkD/K4t40KHCMm56cTXhoYG8ePNYopqZ8Fwzvgd9Ezpy44uruf2Vdbxx2wTCgj1fuCWvuJxHF+zm9VUHGJgcxZtXjaB/0ncXlD5jUBJje8fy53k7eObrDN5YdYDyKgflVd8t6d81wnDDxF4ej7m26PBgXrllPFc/u5Jb5qzl5VvGMbZXLAAFpRXcOmctIYEBPHfDGCK+x8nJBcOSeW3lfv72+S5CggJ447bxLV4Wwx3LaYiIiIh7fX9/yxFpwEebDvL6qv08/IOhLfolNu3wceZtOcTb6zIpOlHJ3Dsm0iWmQ4tiGd8njseuGMGdr63nwXc389iVIzy25tfhojKe+TqD11fvp6LKwexpffjpWSn1ltGP7hDMI5cO44LhXXh/QzaxHUO+M+8tOboD29ev8EqCerLYjs45fFc+u4KbX1zDq7eOZ3CXKO56bT3Zx07w+m3j6R4b7vW42hJjDL+/aAh3vLqOB85OZXTPWF+HJCIiIm6kZE/kJNkFJ/jFe1soLq/ioieX8e+rRzI9NbHBc6y17Mg5zvytOczfeoj03GKMgTE9O/H3y4czuEt0q2KaNTSZn52Vwt+/2E3/pEh+NKNfq653soMFJ3j66z28uSaTaofl4hFd+dGMvk1OdCf3i2fySUMla+z04WLUCZGhvH7rBK54ZgU3PL+KSX3jWb4nn39cPpwxvZTYAKR2jmTRz6b7OgwRERHxACV74jGV1Q6+STvCiQrHKfsSIkMZ26uTx3qoWspayy/e20K1w/LGbRP43cfb+OFLa3ho1kBundr7lHirHZZ5W3J4clE6Ow8dJ8DAhD5x3DixJ2cP7kyiGxdH/9GMfqTlFvO3z3fRN6Ej5wxpXSGMgtIKlu/JZ+GOXD7alI21cNnobtw1vR894vynx6tzdBiv3zaeK59ZyWfbDnHn9L5cOrptLWkhIiIi4glK9sQjrLU88M5m3t9wylKJ3zprUBJ/vHiIWxOi1npvfTZf7z7Cby8YxMS+cbx31yR+9vYm/jRvBztyinj4kqGEBQdSVe3g480HeeKrdPYcKaFfYgQP/2AoZw9O8tjyAsYY/nLpMPbnl3LfW5vo1imcIV2b3mNYWe1g/f5jLE3PY0laHpuzCrAWIkODuHJsd+44rS/dOvlPkldbt07hvDl7At+k5XHV2O6NnyAiIiLiB5TsiUc8umA372/I5p6Z/Th/eJdT9i/amcujC3ZzxqNf86vzB3H56G4+7+XLLSrjdx9vY0zPTt8WFAkPCeLJa0bx76/SeXTBbvYcKebyMd357zcZ7MsvZUDnSJ68ZhSzhnQmwAsVHcOCA3n2htFc/MQybnt5LR/+aHKTkuVjJRVc9exKdh0+TmCAYUT3GO6Z2Z9pKfEM7xZDUKAnV2FpG7rHhnPN+B6+DkNERETEa5Tsidu9ufoA//4qnavGdue+M1PqTOJSkiI5c1ASD767hQfe2czHmw7y50uG+iBaJ2stv/pgK+VVDv562bDvJG7GGO45vT+pnSO5/62N/OqDrQzuEsUz14/mzIFJXknyakuMDOO5G8dw2VMruPXltcy5eRydOobUe3xJeRU3vbSGvfklPHrFcE4fmER0B/9fR05ERETk+87//5wvXrVoVy6//GArp6Uk8IeLhzTYW9cnIYI3Z0/gDxcNZt3+Y5z12BKWZPlm8fBPt+TwxfbD3H9mSr1FSc4e3JlP7pnKq7eM55MfT+Hswd7pzavL4C7R/PvqkezMOc6lTy1nf35JncdVVDm449V1bMkq4ImrR3LJqG5K9ERERES+J5TsidtszS7kR6+tJzUpkievHUVwE4YGBgQYrp/Yiy/um8aI7jG8uLWCb9KOeCHa/8kvLuc3H25jeLdobpnSu8Fje8d3ZEr/eJ8POQXnOnev3Taeo6UVXPKf5aw/cOw7+6sdlvvnbuSbtDweuXQYZw3u7KNIRURERMQXlOyJW2QdK+Xml9YQ0yGYF28e2+yFqrt1Cuf5G8eS3NFw31ubOHK83EORnup3H2+nqKySv142vN3NXRvbK5b37pxEx9Agrn52JZ9tPQQ4h6X+9qNtfLI5h4dmDeCKMSpKIiIiIvJ9ozl7bnC0pILVe48C9pR9/ZMi6duCRbnbk+Nlldz84hrKKqp59c5JJLWwumaHkEDuHBHGH1eVc//cjcy5eZxHhkk6HJZtB4tYknaEb9KOsDLjKPedkUJq50i338sb+iRE8P5dk7j15bXc+do6fnXeIApPVPLKyv3cPq0Pt5/W19chioiIiIgPKNlrpYU7DvPAO5vJL6moc39ggOGO0/rw45n9CQsO9HJ0nmet5efvbiYjr4SXfziu1QlT98gAfn3BIH75/lae/SaDO9yUqBwsOMHStDyWpB1hWXoex0qdcwMHJkdxz8x+3Dm9fSdEcRGhvHHbBO59cyN/+GQ7AFeM6caDswb4ODIRERER8RUley10oqKaP366nddWHXCW37921CmFL6odlpeW7+PJRXv4bOsh/nrZcEb37OSjiOtmraXaYVs8fPHFZfuYt+UQD80awOR+8W6J6ZpxPViWnsffP9/FuN6xjOrR/PespLyKVXvzWbI7j2/SjrDniLOASUJkKDMGJDK1fzyT+8WTGNl21vhrrbDgQJ68dhT/+nI3+SUV/O7CwW1ibqGIiIiI+IaSvRbYml3IPW9uIONICbdN7c3Pzk4lNKjuXru/Xz6cC4Z34RfvbeGyp5dz06Re/L+zUwkP8f1bvzevhDteWUd2wQlmDkhk1pDOTE9NpENI03og1+0/xsPzdnDmoCRmT+vjtriMMfz5kmFsyvyGe97YwKf3TG1yBcnSiirueWMjX+/OpbLaEhYcwLjecVw9rgdT+seTmhTp1wlQYIDh/rNSfR2GiIiIiLQBvs842pFqh+XZJRk8umAXcR1Dee3W8U3qzTotJYHP75vGXz/byYvL9vHljsM8esUIxvaK9ULUdVuy+wh3v76ewADD2YM7s2hXLh9tOkiH4ECmpyYwa2gyZwxMrDcpzS8u5+7X15McE8bfLx/u9gQqukMw/75mJFc8vYKH3tvMk9eMatI9/vjpDhbuPMwtk3szY0Aio3t28svhsyIiIiIijfneJHuHCsuocjjoEt2hRUU/8ovLufctZxn7c4d25uEfDCUmvP6FrE8WERrE7y8awvnDuvDAO5u49r+reOLqkV4vh2+t5fmle3l43g5SkiJ57oYxdI8Np6raweq9R5m3NYfPtx1m/tZDJESGcu8Z/blyTPfvDPOsdljufWsj+SUVvHfnJI+t2zaqRyd+dnYqj8zfyasr93P9xF4NHr9g+2FeX3WA26f14aFzB3okJhERERGR9sLvk72a5OaR+TupcljCQwLplxhBv8QI+idGkpIUwaS+8Q0OXVy77yh3v76Bo6UV/PmSoVw1tnuLe7LG9Y7l/bsmc9NLa7jztfX85dJhXDa6W0tfXrOUVVbzy/e38u76LM4enMSjV4ygo2uJhKDAACb1i2dSv3h+d+EQVmXk8+iC3fzy/a28sHQvD5wzgLMGJWGM4d9fpfFNWh5/vmQoQ7pGezTm2VP7sDIjn998tI3YjqGcNyy5zuNyj5fx83c3Myg5ivvPSvFoTCIiIiIi7YFfJ3uFJyp54J1NfL7tMGcPTuK0lETSco+TnlvM8vR83lufDUBMeDBXj+vBDRN7khzd4dvzrbX895u9PPLZTrp16sD7d01icJfWJzedOobw+q3juf2Vdfzs7U0UlFZw61T3zXmry6HCMu54dR0bMwv4yen9+cnp/evt4QwMMEzqF8/EvnEs2H6YRz7bye2vrGNMz07MGprMvxamccmorlw11vNrtwUEGP5z7ShufGE1P3lzA8GB5pTeUGst/+/tzZSUV/H41SPqnT8pIiIiIvJ94rfJ3tbsQu56bT0HC07wq/MGcsuU3qf0xhWVVbI5s5BXV+7nma/38OySDGYN6cwPp/Smb0IE/+/tTXyx/TDnDO7MXy8fRlSY+4YrdgwN4vmbxnDvmxv546c7KCit5Kdnpbh97ltltYM5y/fxzy/TqHZYnrp2FLOG1t07djJjnInVzAGJzF2bxWNf7uYPn2wnNSmSP148xGuFTsJDgnjhprFc//xq7n59A8/eMJrpqYnf7n95xX6+3n2E3180mH6J7XOtPBERERERd/O7ZM9ayxurM/ntx9uIDQ/hrdsnMLpn3YVQosKCmdI/nin948k8WsrLK/bx5ppMPtmcQ3hIIBVVDv7v/EH8cHIvjyQ2oUGBPHHNKH75/haeWJTOsdIKfn/REALdtJD4ij35/Oajrew+XMz01AR+e8FgesV3bPZ1ggIDuGZ8Dy4e2YX3N2QzPbX+wi2eEhkWzJybx3H1cyu5/ZV1vHjTWCb1iyft8HEenreD6akJXD+hp1djEhERERFpy/wm2Sspr+Krnbm8vyGbr3bmMrV/PP+8cgRxEaFNOr97bDi/PG8Q956Rwrvrs1ixJ59bp/bx+Lp4gQGGP1/iLPby9Nd7yD1ezmNXjiAitOGmWZmRz3NLMkiIDP3fHMSkSLpEh5F7vJw/fbqDjzYdpGtMB569fjRnuubbtUZ4SBDXjvddQhUdHsyrt47nqmdXcMuctTx/4xj+8OkOOoYG8dfLhvn1kgoiIiIiIs3VrpM9h4X3N2Qxb8shluw+QnmVg/iIUP7f2anccVrfFvWQdQwN4oaJvbihkcqP7mSM4cFZA+gcFcofPt3BJf9Zxn9vGEuPuPBTjnU4LE8v2cPfP99FXEQoDoflzTWZ/4s/JBCHhWpruWdmP+6c3q/J6+a1B7EdQ3jt1glc+cwKrvnvKgD+e8MYv1ocXURERETEHdp1snfguIP73tpE56gwrh7Xg3OHJjO6Zye3DYP0tpsm96Z/UiR3vbaeC59cyn+uGcWkWuv4FZRW8NO5m1i4M5fzhyXzyKXDiAgN4mhJBem5xaTlHiftcDHlVQ7uOK0PPeOaP2SzPUiIDOX12yZw04urmdo/njMGJfk6JBERERGRNsdjyZ4x5gXgfCDXWjvEtS0WeAvoBewDrrDWHjPO8Xf/As4FSoGbrLXrG7tHVIjhvbsmMaJbTIvWzmuLJveL56O7J3Pby2u5/oXV/N95A7lxUi82ZzkLzuQeL+P3Fw3m+gk9vx22GNsxhHG9YxnX23eLtHtb5+gw5v9kqoZuioiIiIjUI6DxQ1rsJeCck7Y9CCy01vYHFrqeA8wC+ru+ZgNPNeUGsWGGUT06+U2iV6NnXEfeu2syM1IT+e3H27nhhdVc9vRyAN6+YxI3TPRMwZj2Ru+BiIiIiEj9PJbsWWuXAEdP2nwRMMf1eA5wca3tL1unlUCMMaZp6wP4qYjQIJ69fjQ/ntmPb9LymNo/gU/vmcKI7jG+Dk1ERERERNoBb8/ZS7LW5rgeHwJqJlt1BTJrHZfl2pbD91hAgOGnZ6Vy7fieJEaG+l0PpoiIiIiIeI7PCrRYa60xxjb3PGPMbJxDPUlISGDx4sXuDq1N2unrALykuLj4e9Om3xdqU/+jNvU/alP/ozb1P2pT/+TpdvV2snfYGJNsrc1xDdPMdW3PBrrXOq6ba9sprLXPAs8CpKam2unTp3swXPG2xYsXozb1L2pT/6M29T9qU/+jNvU/alP/5Ol29WSBlrp8BNzoenwj8GGt7TcYpwlAYa3hniIiIiIiItJMnlx64Q1gOhBvjMkCfgM8Asw1xtwC7AeucB0+D+eyC+k4l1642VNxiYiIiIiIfB94LNmz1l5dz67T6zjWAj/yVCwiIiIiIiLfN94exikiIiIiIiJeoGRPRERERETEDynZExERERER8UNK9kRERERERPyQkj0RERERERE/pGRPRERERETEDxnnqgftkzHmOLDL13GIW8UDeb4OQtxKbep/1Kb+R23qf9Sm/kdt6p/c0a49rbUJde3w2Dp7XrLLWjvG10GI+xhj1qpN/Yva1P+oTf2P2tT/qE39j9rUP3m6XTWMU0RERERExA8p2RMREREREfFD7T3Ze9bXAYjbqU39j9rU/6hN/Y/a1P+oTf2P2tQ/ebRd23WBFhEREREREalbe+/ZExERERERkTq022TPGHOOMWaXMSbdGPOgr+OR5jPGdDfGLDLGbDfGbDPG/MS1PdYYs8AYk+b63snXsUrTGWMCjTEbjDGfuJ73Nsascn1W3zLGhPg6RmkeY0yMMeYdY8xOY8wOY8xEfU7bN2PMfa6fu1uNMW8YY8L0WW1fjDEvGGNyjTFba22r83NpnB53te1mY8wo30Uu9amnTf/m+tm72RjzvjEmpta+h1xtussYc7ZPgpYG1dWmtfb91BhjjTHxruce+Zy2y2TPGBMIPAnMAgYBVxtjBvk2KmmBKuCn1tpBwATgR652fBBYaK3tDyx0PZf24yfAjlrP/wI8Zq3tBxwDbvFJVNIa/wI+s9YOAIbjbF99TtspY0xX4B5gjLV2CBAIXIU+q+3NS8A5J22r73M5C+jv+poNPOWlGKV5XuLUNl0ADLHWDgN2Aw8BuH5fugoY7DrnP67fj6VteYlT2xRjTHfgLOBArc0e+Zy2y2QPGAekW2szrLUVwJvART6OSZrJWptjrV3venwc5y+QXXG25RzXYXOAi30SoDSbMaYbcB7wX9dzA8wE3nEdovZsZ4wx0cA04HkAa22FtbYAfU7buyCggzEmCAgHctBntV2x1i4Bjp60ub7P5UXAy9ZpJRBjjEn2SqDSZHW1qbX2C2ttlevpSqCb6/FFwJvW2nJr7V4gHefvx9KG1PM5BXgMeACoXTzFI5/T9prsdQUyaz3Pcm2TdsoY0wsYCawCkqy1Oa5dh4AkX8UlzfZPnD+8HK7ncUBBrf+o9Fltf3oDR4AXXcNz/2uM6Yg+p+2WtTYb+DvOvyjnAIXAOvRZ9Qf1fS71e5N/+CEw3/VYbdpOGWMuArKttZtO2uWRNm2vyZ74EWNMBPAucK+1tqj2PussF6uSse2AMeZ8INdau87XsYhbBQGjgKestSOBEk4asqnPafvimsd1Ec5EvgvQkTqGGUn7ps+lfzHG/BLn9JfXfB2LtJwxJhz4BfBrb92zvSZ72UD3Ws+7ubZJO2OMCcaZ6L1mrX3PtflwTbe163uur+KTZpkMXGiM2YdzaPVMnHO9YlxDxUCf1fYoC8iy1q5yPX8HZ/Knz2n7dQaw11p7xFpbCbyH8/Orz2r7V9/nUr83tWPGmJuA84Fr7f/WTFObtk99cf6hbZPr96VuwHpjTGc81KbtNdlbA/R3VQ4LwTlB9SMfxyTN5JrP9Tyww1r7aK1dHwE3uh7fCHzo7dik+ay1D1lru1lre+H8TH5lrb0WWARc5jpM7dnOWGsPAZnGmFTXptOB7ehz2p4dACYYY8JdP4dr2lSf1favvs/lR8ANrmp/E4DCWsM9pQ0zxpyDc3rEhdba0lq7PgKuMsaEGmN64yzqsdoXMUrTWWu3WGsTrbW9XL8vZQGjXP/XeuRz2m4XVTfGnItzflAg8IK19k++jUiayxgzBfgG2ML/5nj9Aue8vblAD2A/cIW1tq7JrdJGGWOmAz+z1p5vjOmDs6cvFtgAXGetLfdheNJMxpgROIvuhAAZwM04/1ioz2k7ZYz5HXAlzmFhG4Bbcc4N0We1nTDGvAFMB+KBw8BvgA+o43PpSuqfwDlctxS42Vq71gdhSwPqadOHgFAg33XYSmvtHa7jf4lzHl8Vzqkw80++pvhWXW1qrX2+1v59OCsj53nqc9pukz0RERERERGpX3sdxikiIiIiIiINULInIiIiIiLih5TsiYiIiIiI+CEleyIiIiIiIn5IyZ6IiIiIiIgfUrInIiLfS8YYa4x5tdbzIGPMEWPMJy28Xowx5q5az6e39FoiIiLuoGRPRES+r0qAIcaYDq7nZwLZrbheDHBXYweJiIh4i5I9ERH5PpsHnOd6fDXwRs0OY0ysMeYDY8xmY8xKY8ww1/bfGmNeMMYsNsZkGGPucZ3yCNDXGLPRGPM317YIY8w7xpidxpjXXIvmYox5xBiz3XXtv3vnpYqIyPdNkK8DEBER8aE3gV+7hlsOA14Aprr2/Q7YYK292BgzE3gZGOHaNwCYAUQCu4wxTwEPAkOstSPAOYwTGAkMBg4Cy4DJxpgdwA+AAdZaa4yJ8exLFBGR7yv17ImIyPeWtXYz0Atnr968k3ZPAV5xHfcVEGeMiXLt+9RaW26tzQNygaR6brHaWptlrXUAG133KgTKgOeNMZcApW57QSIiIrUo2RMRke+7j4C/U2sIZxOU13pcTf0jZU45zlpbBYwD3gHOBz5rxn1FRESaTMmeiIh8370A/M5au+Wk7d8A18K3QzLzrLVFDVznOM5hnQ0yxkQA0dbaecB9wPAWxCwiItIozdkTEZHvNWttFvB4Hbt+C7xgjNmMc6jljY1cJ98Ys8wYsxWYD3xaz6GRwIfGmDDAAPe3NHYREZGGGGutr2MQERERERERN9MwThERERERET+kZE9ERERERMQPKdkTERERERHxQ0r2RERERERE/JCSPRERERERET+kZE9ERERERMQPKdkTERERERHxQ0r2RERERERE/ND/B5C+iqKY2XMNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "预测\n",
    "注意，test_input中包含12个数据，\n",
    "在for循环中，12个数据将用于对测试集的第一个数据进行预测，然后将预测值附加到test_inputs列表中。\n",
    "在第二次迭代中，最后12个数据将再次用作输入，并进行新的预测，然后 将第二次预测的新值再次添加到列表中。\n",
    "由于测试集中有12个元素，因此该循环将执行12次。\n",
    "循环结束后，test_inputs列表将包含24个数据，其中，最后12个数据将是测试集的预测值。\n",
    "\"\"\"\n",
    "fut_pred = 12\n",
    "test_inputs = train_data_normalized[-train_window:].tolist()#首先打印出数据列表的最后12个值\n",
    "print(test_inputs)\n",
    "\n",
    "#更改模型为测试或者验证模式\n",
    "model.eval()#把training属性设置为false,使模型处于测试或验证状态\n",
    "for i in range(fut_pred):\n",
    "    seq = torch.FloatTensor(test_inputs[-train_window:])\n",
    "    with torch.no_grad():\n",
    "        model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "        test_inputs.append(model(seq).item())\n",
    "#打印最后的12个预测值\n",
    "print(test_inputs[fut_pred:])\n",
    "#由于对训练集数据进行了标准化，因此预测数据也是标准化了的\n",
    "#需要将归一化的预测值转换为实际的预测值。通过inverse_transform实现\n",
    "actual_predictions = scaler.inverse_transform(np.array(test_inputs[train_window:]).reshape(-1, 1))\n",
    "print(actual_predictions)\n",
    "\n",
    "\"\"\"\n",
    "根据实际值，绘制预测值\n",
    "\"\"\"\n",
    "x = np.arange(132, 132+fut_pred, 1)\n",
    "plt.title('Month vs Passenger')\n",
    "plt.ylabel('Total Passengers')\n",
    "plt.xlabel('Months')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x', tight=True)\n",
    "plt.plot(flight_data['passengers'])\n",
    "plt.plot(x, actual_predictions)\n",
    "plt.show()\n",
    "#绘制最近12个月的实际和预测乘客数量,以更大的尺度观测数据\n",
    "plt.title('Month vs Passenger')\n",
    "plt.ylabel('Total Passengers')\n",
    "plt.xlabel('Months')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x', tight=True)\n",
    "plt.plot(flight_data['passengers'][-train_window:])\n",
    "plt.plot(x, actual_predictions)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0a9e8949fc24b5f62ea486aa0e3668f251ab86c129ace0f639a1ad6db239a700"
  },
  "kernelspec": {
   "display_name": "Python 3.7.12 ('Pytorch')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
